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Filter Events:

SUN 17 JUL

6 a.m.

7 a.m.

Registration Desk:

Registration Check-in Desk

(ends 4:00 PM)

9 a.m.

Expo Talk Panel:

Challenges Of Applying Graph Neural Networks

(ends 1:45 PM)

Expo Talk Panel:

Enabling Hand Gesture Customization on Wrist-Worn Devices

(ends 12:00 PM)

Expo Workshop:

Real World RL with Vowpal Wabbit and Azure Personalizer

(ends 11:50 AM)

Expo Demonstration:

AEPsych: active learning for human perception and preferences

(ends 2:00 PM)

Expo Demonstration:

TorchRL: the PyTorch RL Domain library

(ends 2:00 PM)

9:30 a.m.

Expo Talk Panel:

Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer

(ends 10:15 AM)

10:20 a.m.

Expo Demonstration:

Creating a scalable, reproducible, and reliable environment for model development with AstraZeneca and W&B.

(ends 11:20 AM)

11 a.m.

Cancelled:

Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training

(ends 11:45 AM)

Expo Demonstration:

Enabling Hand Gesture Customization on Wrist-Worn Devices

(ends 12:00 PM)

11:30 a.m.

Expo Talk Panel:

Machine learning for drug discovery: Challenges and opportunities

(ends 1:00 PM)

Coffee Break

12:15 p.m.

Expo Demonstration:

Robust and Fast Detection of Toxic Speech Content via Machine Learning

(ends 1:00 PM)

1:15 p.m.

Expo Talk Panel:

Towards Robust Waveform-Based Acoustic Models

(ends 2:00 PM)

2:30 p.m.

Opening Reception - Catered:

Opening Reception

(ends 4:00 PM)

MON 18 JUL

4 a.m.

Registration Desk:

Registration Check-in Desk

(ends 3:00 PM)

5 a.m.

Affinity Workshop:

LatinX in AI (LXAI) LXAI Research Workshop

(ends 5:00 PM)

5:30 a.m.

Affinity Workshop:

Women in Machine Learning (WiML) Un-Workshop

(ends 3:40 PM)

6 a.m.

Affinity Workshop:

New In Machine Learning (NewInML)

(ends 1:30 PM)

6:30 a.m.

Tutorial:

Quantitative Reasoning About Data Privacy in Machine Learning

(ends 8:30 AM)

Tutorial:

Validity, Reliability, and Significance: A Tutorial on Statistical Methods for Reproducible Machine Learning

(ends 8:45 AM)

Tutorial:

Causality and Deep Learning: Synergies, Challenges and the Future

(ends 8:30 AM)

7 a.m.

Coffee Break

8 a.m.

9 a.m.

Lunch Break - on your own

10 a.m.

Tutorial:

Learning for Interactive Agents

(ends 12:00 PM)

Tutorial:

Bridging Learning and Decision Making

(ends 12:00 PM)

Tutorial:

Climate Change and Machine Learning: Opportunities, Challenges, and Considerations

(ends 12:00 PM)

11 a.m.

noon

Coffee Break

12:30 p.m.

Tutorial:

Causal Fairness Analysis

(ends 2:30 PM)

Tutorial:

Sampling as First-Order Optimization over a space of probability measures

(ends 2:30 PM)

Tutorial:

Welcome to the "Big Model" Era: Techniques and Systems to Train and Serve Bigger Models

(ends 2:50 PM)

4 p.m.

TUE 19 JUL

3:30 a.m.

Breakfast on your own

4 a.m.

Registration Desk:

Registration Check-in Desk

(ends 4:00 PM)

5:45 a.m.

Remarks:

Welcome by Chairs

(ends 6:00 AM)

6 a.m.

Invited Talk:

Towards a Mathematical Theory of Machine Learning

Weinan E

(ends 7:00 AM)

7 a.m.

Coffee Break

7:30 a.m.

Social Aspects [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Differentially Private Approximate Quantiles

[7:35] Fairness Interventions as (Dis)Incentives for Strategic Manipulation

[7:40] Robust Models Are More Interpretable Because Attributions Look Normal

[7:45] Sequential Covariate Shift Detection Using Classifier Two-Sample Tests

[7:50] A Joint Exponential Mechanism For Differentially Private Top-$k$

[7:55] Transfer Learning In Differential Privacy's Hybrid-Model

[8:00] Robust Kernel Density Estimation with Median-of-Means principle

Oral s 8:05-8:25

[8:05] Bounding Training Data Reconstruction in Private (Deep) Learning

Spotlight s 8:25-9:00

[8:25] Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks

[8:30] FriendlyCore: Practical Differentially Private Aggregation

[8:35] ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder

[8:40] Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

[8:45] Public Data-Assisted Mirror Descent for Private Model Training

[8:50] Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions

[8:55] Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

(ends 9:00 AM)

Probabilistic Methods/Applications [7:30-9:00]

Oral s 7:30-7:50

[7:30] Tackling covariate shift with node-based Bayesian neural networks

Spotlight s 7:50-8:10

[7:50] Why the Rich Get Richer? On the Balancedness of Random Partition Models

[7:55] A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation

[8:00] Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems

[8:05] Calibrated Learning to Defer with One-vs-All Classifiers

Oral s 8:10-8:30

[8:10] Tractable Uncertainty for Structure Learning

Spotlight s 8:30-8:55

[8:30] DNA: Domain Generalization with Diversified Neural Averaging

[8:35] Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

[8:40] DynaMixer: A Vision MLP Architecture with Dynamic Mixing

[8:45] Channel Importance Matters in Few-Shot Image Classification

[8:50] Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

(ends 9:00 AM)

Reinforcement Learning [7:30-9:00]

Spotlight s 7:30-8:00

[7:30] Dynamic Regret of Online Markov Decision Processes

[7:35] On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games

[7:40] Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

[7:45] Provable Reinforcement Learning with a Short-Term Memory

[7:50] Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer

[7:55] Mirror Learning: A Unifying Framework of Policy Optimisation

Oral s 8:00-8:20

[8:00] Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP

Spotlight s 8:20-8:50

[8:20] Learning Infinite-horizon Average-reward Markov Decision Process with Constraints

[8:25] A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

[8:30] Langevin Monte Carlo for Contextual Bandits

[8:35] Prompting Decision Transformer for Few-Shot Policy Generalization

[8:40] Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning

[8:45] Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

(ends 9:00 AM)

Deep Learning: Robustness [7:30-9:00]

Oral s 7:30-7:50

[7:30] Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them

Spotlight s 7:50-8:10

[7:50] ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks

[7:55] Provably Adversarially Robust Nearest Prototype Classifiers

[8:00] Certifying Out-of-Domain Generalization for Blackbox Functions

[8:05] Intriguing Properties of Input-Dependent Randomized Smoothing

Oral s 8:10-8:30

[8:10] To Smooth or Not? When Label Smoothing Meets Noisy Labels

Spotlight s 8:30-8:55

[8:30] Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

[8:35] On the Generalization Analysis of Adversarial Learning

[8:40] Demystifying the Adversarial Robustness of Random Transformation Defenses

[8:45] Double Sampling Randomized Smoothing

[8:50] TPC: Transformation-Specific Smoothing for Point Cloud Models

(ends 9:00 AM)

APP: Language, Speech and Dialog [7:30-9:00]

Spotlight s 7:30-8:00

[7:30] Certified Robustness Against Natural Language Attacks by Causal Intervention

[7:35] A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

[7:40] On the Learning of Non-Autoregressive Transformers

[7:45] Latent Diffusion Energy-Based Model for Interpretable Text Modelling

[7:50] UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

[7:55] Black-Box Tuning for Language-Model-as-a-Service

Oral s 8:00-8:20

[8:00] Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information

Spotlight s 8:20-9:00

[8:20] Co-training Improves Prompt-based Learning for Large Language Models

[8:25] Directed Acyclic Transformer for Non-Autoregressive Machine Translation

[8:30] StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models

[8:35] Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology

[8:40] Generative Cooperative Networks for Natural Language Generation

[8:45] What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?

[8:50] Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding

[8:55] ROCK: Causal Inference Principles for Reasoning about Commonsense Causality

(ends 9:00 AM)

Optimization: Convex [7:30-9:00]

Oral s 7:30-7:50

[7:30] Exact Optimal Accelerated Complexity for Fixed-Point Iterations

Spotlight s 7:50-8:15

[7:50] Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions

[7:55] NysADMM: faster composite convex optimization via low-rank approximation

[8:00] FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

[8:05] Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

[8:10] Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding

Oral s 8:15-8:35

[8:15] Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems

Spotlight s 8:35-9:00

[8:35] Only tails matter: Average-Case Universality and Robustness in the Convex Regime

[8:40] Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity

[8:45] Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets

[8:50] Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time

[8:55] Active Sampling for Min-Max Fairness

(ends 9:00 AM)

Theory: Online Learning/Bandits [7:30-9:00]

Oral s 7:30-7:50

[7:30] Online Learning for Min Sum Set Cover and Pandora’s Box

Spotlight s 7:50-8:15

[7:50] Smoothed Adversarial Linear Contextual Bandits with Knapsacks

[7:55] Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

[8:00] Thompson Sampling for (Combinatorial) Pure Exploration

[8:05] Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness

[8:10] Rotting Infinitely Many-Armed Bandits

Oral s 8:15-8:35

[8:15] Batched Dueling Bandits

Spotlight s 8:35-9:00

[8:35] Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent

[8:40] Consistent Polyhedral Surrogates for Top-k Classification and Variants

[8:45] Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

[8:50] Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

[8:55] Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback

(ends 9:00 AM)

Transfer/Multitask/Meta Learning [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Multi-Task Learning as a Bargaining Game

[7:35] Frustratingly Easy Transferability Estimation

[7:40] Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

[7:45] A Difference Standardization Method for Mutual Transfer Learning

[7:50] Improving Task-free Continual Learning by Distributionally Robust Memory Evolution

[7:55] A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity

[8:00] Sparse Invariant Risk Minimization

Oral s 8:05-8:25

[8:05] Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Spotlight s 8:25-9:00

[8:25] A Closer Look at Smoothness in Domain Adversarial Training

[8:30] Balancing Discriminability and Transferability for Source-Free Domain Adaptation

[8:35] Model Agnostic Sample Reweighting for Out-of-Distribution Learning

[8:40] Zero-shot AutoML with Pretrained Models

[8:45] Efficient Variance Reduction for Meta-learning

[8:50] Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder

[8:55] Partial disentanglement for domain adaptation

(ends 9:00 AM)

Deep Learning [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Structural Entropy Guided Graph Hierarchical Pooling

[7:35] Self-Supervised Representation Learning via Latent Graph Prediction

[7:40] DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

[7:45] Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

[7:50] Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning

[7:55] Analyzing and Mitigating Interference in Neural Architecture Search

[8:00] Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees

Oral s 8:05-8:25

[8:05] Unified Scaling Laws for Routed Language Models

Spotlight s 8:25-9:00

[8:25] DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks

[8:30] A deep convolutional neural network that is invariant to time rescaling

[8:35] LyaNet: A Lyapunov Framework for Training Neural ODEs

[8:40] Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

[8:45] On Collective Robustness of Bagging Against Data Poisoning

[8:50] Hindering Adversarial Attacks with Implicit Neural Representations

[8:55] From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

(ends 9:00 AM)

Deep Learning: Generative Models/Autoencoders [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling

[7:35] ButterflyFlow: Building Invertible Layers with Butterfly Matrices

[7:40] Controlling Conditional Language Models without Catastrophic Forgetting

[7:45] GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

[7:50] Structure-preserving GANs

[7:55] DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

[8:00] Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models

Oral s 8:05-8:25

[8:05] Equivariant Diffusion for Molecule Generation in 3D

Spotlight s 8:25-9:00

[8:25] Forward Operator Estimation in Generative Models with Kernel Transfer Operators

[8:30] Conditional GANs with Auxiliary Discriminative Classifier

[8:35] Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images

[8:40] Matching Normalizing Flows and Probability Paths on Manifolds

[8:45] Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization

[8:50] Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

[8:55] Region-Based Semantic Factorization in GANs

(ends 9:00 AM)

9 a.m.

Lunch Break - on your own

10:30 a.m.

DL: Algorithms [10:30-12:00]

Spotlight s 10:30-11:00

[10:30] Online Continual Learning through Mutual Information Maximization

[10:35] Learning Iterative Reasoning through Energy Minimization

[10:40] DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

[10:45] PoF: Post-Training of Feature Extractor for Improving Generalization

[10:50] Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation

[10:55] Set Based Stochastic Subsampling

Oral s 11:00-11:20

[11:00] Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Spotlight s 11:20-11:55

[11:20] Generalizing to New Physical Systems via Context-Informed Dynamics Model

[11:25] Self-conditioning Pre-Trained Language Models

[11:30] TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

[11:35] Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

[11:40] Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning

[11:45] Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

[11:50] When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

(ends 12:00 PM)

SA: Accountability, Transparency and Interpretability [10:30-12:00]

Spotlight s 10:30-11:05

[10:30] Meaningfully debugging model mistakes using conceptual counterfactual explanations

[10:35] Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

[10:40] Robust Counterfactual Explanations for Tree-Based Ensembles

[10:45] A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

[10:50] Estimating and Penalizing Induced Preference Shifts in Recommender Systems

[10:55] Framework for Evaluating Faithfulness of Local Explanations

[11:00] A Consistent and Efficient Evaluation Strategy for Attribution Methods

Oral s 11:05-11:25

[11:05] Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four

Spotlight s 11:25-12:00

[11:25] Label-Descriptive Patterns and Their Application to Characterizing Classification Errors

[11:30] XAI for Transformers: Better Explanations through Conservative Propagation

[11:35] Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

[11:40] Interpretable Off-Policy Learning via Hyperbox Search

[11:45] Neuron Dependency Graphs: A Causal Abstraction of Neural Networks

[11:50] On the Adversarial Robustness of Causal Algorithmic Recourse

[11:55] Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

(ends 12:00 PM)

APP: Computer Vision [10:30-12:00]

Spotlight s 10:30-11:00

[10:30] Robust Group Synchronization via Quadratic Programming

[10:35] UAST: Uncertainty-Aware Siamese Tracking

[10:40] You Only Cut Once: Boosting Data Augmentation with a Single Cut

[10:45] Generative Modeling for Multi-task Visual Learning

[10:50] HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

[10:55] Parametric Visual Program Induction with Function Modularization

Oral s 11:00-11:20

[11:00] Path-Gradient Estimators for Continuous Normalizing Flows

Spotlight s 11:20-11:55

[11:20] Variational Feature Pyramid Networks

[11:25] Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning

[11:30] VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix

[11:35] Neural Implicit Dictionary Learning via Mixture-of-Expert Training

[11:40] Time Is MattEr: Temporal Self-supervision for Video Transformers

[11:45] Benchmarking and Analyzing Point Cloud Classification under Corruptions

[11:50] Understanding The Robustness in Vision Transformers

(ends 12:00 PM)

Theory [10:30-12:00]

Oral s 10:30-10:50

[10:30] Learning Mixtures of Linear Dynamical Systems

Spotlight s 10:50-11:15

[10:50] Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances

[10:55] Residual-Based Sampling for Online Outlier-Robust PCA

[11:00] Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

[11:05] Streaming Algorithms for Support-Aware Histograms

[11:10] Power-Law Escape Rate of SGD

Oral s 11:15-11:35

[11:15] Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model

Spotlight s 11:35-12:00

[11:35] Faster Algorithms for Learning Convex Functions

[11:40] Feature selection using e-values

[11:45] ActiveHedge: Hedge meets Active Learning

[11:50] One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes

[11:55] Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm

(ends 12:00 PM)

MISC: Unsupervised and Semi-supervised Learning [10:30-12:00]

Spotlight s 10:30-11:00

[10:30] An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

[10:35] Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data

[10:40] Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding

[10:50] Meta-Learning Hypothesis Spaces for Sequential Decision-making

[10:55] A Tighter Analysis of Spectral Clustering, and Beyond

Oral s 11:00-11:20

[11:00] Online Active Regression

Spotlight s 11:20-11:55

[11:20] On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

[11:25] Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework

[11:30] Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

[11:35] Confidence Score for Source-Free Unsupervised Domain Adaptation

[11:40] Gradient Based Clustering

[11:45] Global Optimization of K-Center Clustering

[11:50] Latent Outlier Exposure for Anomaly Detection with Contaminated Data

(ends 12:00 PM)

PM: Gaussian Processes [10:30-12:00]

Spotlight s 10:30-11:00

[10:30] Additive Gaussian Processes Revisited

[10:35] Probabilistic ODE Solutions in Millions of Dimensions

[10:40] Adaptive Gaussian Process Change Point Detection

[10:45] Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

[10:50] Fenrir: Physics-Enhanced Regression for Initial Value Problems

[10:55] Variational nearest neighbor Gaussian process

Oral s 11:00-11:20

[11:00] Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Spotlight s 11:20-11:50

[11:20] Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty

[11:25] Bayesian Optimization under Stochastic Delayed Feedback

[11:30] Bayesian Optimization for Distributionally Robust Chance-constrained Problem

[11:35] Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity

[11:40] Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning

[11:45] Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation

(ends 12:00 PM)

Reinforcement Learning: Deep/Batch/Offline [10:30-12:00]

Oral s 10:30-10:50

[10:30] Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Spotlight s 10:50-11:15

[10:50] AnyMorph: Learning Transferable Polices By Inferring Agent Morphology

[10:55] DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

[11:00] Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

[11:05] Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

[11:10] CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer

Oral s 11:15-11:35

[11:15] Offline RL Policies Should Be Trained to be Adaptive

Spotlight s 11:35-12:00

[11:35] Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

[11:40] PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration

[11:45] Supervised Off-Policy Ranking

[11:50] The Primacy Bias in Deep Reinforcement Learning

[11:55] Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

(ends 12:00 PM)

Optimization [10:30-12:00]

Oral s 10:30-10:50

[10:30] Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

Spotlight s 10:50-11:10

[10:50] Stochastic Reweighted Gradient Descent

[10:55] Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood

[11:00] Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

[11:05] FedNL: Making Newton-Type Methods Applicable to Federated Learning

Oral s 11:10-11:30

[11:10] Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation

Spotlight s 11:30-11:55

[11:30] Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization

[11:35] Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems

[11:40] Probabilistic Bilevel Coreset Selection

[11:45] Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs

[11:50] On Implicit Bias in Overparameterized Bilevel Optimization

(ends 12:00 PM)

DL: Graph Neural Networks [10:30-12:00]

Spotlight s 10:30-11:05

[10:30] pathGCN: Learning General Graph Spatial Operators from Paths

[10:35] Graph-Coupled Oscillator Networks

[10:40] HousE: Knowledge Graph Embedding with Householder Parameterization

[10:45] Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

[10:50] ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

[10:55] G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters

[11:00] SpeqNets: Sparsity-aware permutation-equivariant graph networks

Oral s 11:05-11:25

[11:05] data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

Spotlight s 11:25-11:55

[11:25] Position Prediction as an Effective Pretraining Strategy

[11:30] Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering

[11:35] Deep and Flexible Graph Neural Architecture Search

[11:40] GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks

[11:45] Large-Scale Graph Neural Architecture Search

[11:50] Optimization-Induced Graph Implicit Nonlinear Diffusion

(ends 12:00 PM)

Theory: Bandits/RL/Everything Else [10:30-12:00]

Oral s 10:30-10:50

[10:30] Robustness Implies Generalization via Data-Dependent Generalization Bounds

Spotlight s 10:50-11:15

[10:50] Learning to Hash Robustly, Guaranteed

[10:55] Policy Gradient Method For Robust Reinforcement Learning

[11:00] A query-optimal algorithm for finding counterfactuals

[11:05] Linear Bandit Algorithms with Sublinear Time Complexity

[11:10] Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra

Oral s 11:15-11:35

[11:15] Individual Preference Stability for Clustering

Spotlight s 11:35-12:00

[11:35] Correlated Quantization for Distributed Mean Estimation and Optimization

[11:40] Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms

[11:45] Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

[11:50] The Algebraic Path Problem for Graph Metrics

[11:55] Steerable 3D Spherical Neurons

(ends 12:00 PM)

11 a.m.

noon

Coffee Break

12:30 p.m.

Award:

Test of Time Award

(ends 1:00 PM)

1 p.m.

Short Break

1:15 p.m.

Deep Learning [1:15-2:45]

Spotlight s 1:15-1:50

[1:15] Prototype Based Classification from Hierarchy to Fairness

[1:20] Neural-Symbolic Models for Logical Queries on Knowledge Graphs

[1:25] Deep Probability Estimation

[1:30] Uncertainty Modeling in Generative Compressed Sensing

[1:35] Going Deeper into Permutation-Sensitive Graph Neural Networks

[1:40] Learning from Counterfactual Links for Link Prediction

[1:45] Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

Oral s 1:50-2:10

[1:50] Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations

Spotlight s 2:10-2:45

[2:10] Principal Component Flows

[2:15] Bit Prioritization in Variational Autoencoders via Progressive Coding

[2:20] Generative Flow Networks for Discrete Probabilistic Modeling

[2:25] Diffusion bridges vector quantized variational autoencoders

[2:30] Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization

[2:35] Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

[2:40] Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

(ends 2:45 PM)

MISC: Causality [1:15-2:45]

Spotlight s 1:15-1:50

[1:15] Coordinated Double Machine Learning

[1:20] Exploiting Independent Instruments: Identification and Distribution Generalization

[1:25] Partial Counterfactual Identification from Observational and Experimental Data

[1:30] On Measuring Causal Contributions via do-interventions

[1:35] The Role of Deconfounding in Meta-learning

[1:40] CITRIS: Causal Identifiability from Temporal Intervened Sequences

[1:45] Online Balanced Experimental Design

Oral s 1:50-2:10

[1:50] Minimum Cost Intervention Design for Causal Effect Identification

Spotlight s 2:10-2:45

[2:10] Causal structure-based root cause analysis of outliers

[2:15] Instrumental Variable Regression with Confounder Balancing

[2:20] Causal Transformer for Estimating Counterfactual Outcomes

[2:25] Causal Inference Through the Structural Causal Marginal Problem

[2:30] Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

[2:35] Matching Learned Causal Effects of Neural Networks with Domain Priors

[2:40] Inferring Cause and Effect in the Presence of Heteroscedastic Noise

(ends 2:45 PM)

SA: Trustworthy Machine Learning [1:15-2:45]

Oral s 1:15-1:35

[1:15] POEM: Out-of-Distribution Detection with Posterior Sampling

Spotlight s 1:35-1:55

[1:35] Selective Network Linearization for Efficient Private Inference

[1:40] Efficient Computation of Higher-Order Subgraph Attribution via Message Passing

[1:45] A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

[1:50] Modular Conformal Calibration

Oral s 1:55-2:15

[1:55] Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

Spotlight s 2:15-2:40

[2:15] Context-Aware Drift Detection

[2:20] Accelerating Shapley Explanation via Contributive Cooperator Selection

[2:25] An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

[2:30] DAVINZ: Data Valuation using Deep Neural Networks at Initialization

[2:35] Sample Efficient Learning of Predictors that Complement Humans

(ends 2:45 PM)

T: Learning/Deep Learning Theory [1:15-2:45]

Oral s 1:15-1:35

[1:15] H-Consistency Bounds for Surrogate Loss Minimizers

Spotlight s 1:35-2:00

[1:35] Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent

[1:40] The Teaching Dimension of Regularized Kernel Learners

[1:45] Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

[1:50] TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm

[1:55] Multiclass learning with margin: exponential rates with no bias-variance trade-off

Oral s 2:00-2:20

[2:00] Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models

Spotlight s 2:20-2:45

[2:20] High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails

[2:25] An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn

[2:30] Inductive Biases and Variable Creation in Self-Attention Mechanisms

[2:35] Topology-aware Generalization of Decentralized SGD

[2:40] Understanding Gradient Descent on the Edge of Stability in Deep Learning

(ends 2:45 PM)

APP: Neuroscience, Cognitive Science [1:15-2:45]

Spotlight s 1:15-1:45

[1:15] Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

[1:20] On the Effects of Artificial Data Modification

[1:25] Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage

[1:30] How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

[1:35] Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

[1:40] How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective

Oral s 1:45-2:05

[1:45] Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Spotlight s 2:05-2:45

[2:05] Describing Differences between Text Distributions with Natural Language

[2:10] Distinguishing rule- and exemplar-based generalization in learning systems

[2:15] Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning

[2:20] A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications

[2:25] Minimizing Control for Credit Assignment with Strong Feedback

[2:30] Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech

[2:35] Towards Scaling Difference Target Propagation by Learning Backprop Targets

[2:40] Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

(ends 2:45 PM)

PM: Monte Carlo and Sampling Methods [1:15-2:45]

Oral s 1:15-1:35

[1:15] Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes

Spotlight s 1:35-2:00

[1:35] Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

[1:40] Hessian-Free High-Resolution Nesterov Acceleration For Sampling

[1:45] LSB: Local Self-Balancing MCMC in Discrete Spaces

[1:50] A Langevin-like Sampler for Discrete Distributions

[1:55] Scalable Spike-and-Slab

Oral s 2:00-2:20

[2:00] Nonparametric Involutive Markov Chain Monte Carlo

Spotlight s 2:20-2:45

[2:20] Continual Repeated Annealed Flow Transport Monte Carlo

[2:25] Algorithms for the Communication of Samples

[2:30] Low-Precision Stochastic Gradient Langevin Dynamics

[2:35] Fast Relative Entropy Coding with A* coding

[2:40] Accurate Quantization of Measures via Interacting Particle-based Optimization

(ends 2:45 PM)

OPT: Non-Convex [1:15-2:45]

Spotlight s 1:15-1:45

[1:15] Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective

[1:20] Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering

[1:25] Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(\epsilon^{-7/4})$ Complexity

[1:30] Understanding the unstable convergence of gradient descent

[1:35] Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

[1:40] Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm

Oral s 1:45-2:05

[1:45] FedNest: Federated Bilevel, Minimax, and Compositional Optimization

Spotlight s 2:05-2:35

[2:05] AdaGrad Avoids Saddle Points

[2:10] Fast and Provable Nonconvex Tensor RPCA

[2:15] On Convergence of Gradient Descent Ascent: A Tight Local Analysis

[2:20] Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness

[2:25] A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization

[2:30] Anticorrelated Noise Injection for Improved Generalization

(ends 2:45 PM)

RL: Multi-agent [1:15-2:45]

Spotlight s 1:15-1:45

[1:15] Model-Free Opponent Shaping

[1:20] Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning

[1:25] Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

[1:30] Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning

[1:35] Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

[1:40] Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning

Oral s 1:45-2:05

[1:45] Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

Spotlight s 2:05-2:45

[2:05] Self-Organized Polynomial-Time Coordination Graphs

[2:10] Individual Reward Assisted Multi-Agent Reinforcement Learning

[2:15] Generalized Beliefs for Cooperative AI

[2:20] Greedy when Sure and Conservative when Uncertain about the Opponents

[2:25] Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning

[2:30] Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy

[2:35] Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

[2:40] Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

(ends 2:45 PM)

DL: Sequential Models [1:15-2:45]

Spotlight s 1:15-1:45

[1:15] Modeling Irregular Time Series with Continuous Recurrent Units

[1:20] TACTiS: Transformer-Attentional Copulas for Time Series

[1:25] CerDEQ: Certifiable Deep Equilibrium Model

[1:30] Approximately Equivariant Networks for Imperfectly Symmetric Dynamics

[1:35] IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data

[1:40] GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing

Oral s 1:45-2:05

[1:45] Neural Laplace: Learning diverse classes of differential equations in the Laplace domain

Spotlight s 2:05-2:45

[2:05] Improving Language Models by Retrieving from Trillions of Tokens

[2:10] Closed-Form Diffeomorphic Transformations for Time Series Alignment

[2:15] Removing Batch Normalization Boosts Adversarial Training

[2:20] Forget-free Continual Learning with Winning Subnetworks

[2:25] FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

[2:30] Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers

[2:35] On the Practicality of Deterministic Epistemic Uncertainty

[2:40] Combining Diverse Feature Priors

(ends 2:45 PM)

Theory [1:15-2:45]

Oral s 1:15-1:35

[1:15] Cooperative Online Learning in Stochastic and Adversarial MDPs

Spotlight s 1:35-2:00

[1:35] Simple and near-optimal algorithms for hidden stratification and multi-group learning

[1:40] Being Properly Improper

[1:45] Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks

[1:50] On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions

[1:55] Nearly Optimal Policy Optimization with Stable at Any Time Guarantee

Oral s 2:00-2:20

[2:00] Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces

Spotlight s 2:20-2:45

[2:20] Minimax M-estimation under Adversarial Contamination

[2:25] Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits

[2:30] Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

[2:35] Boosting Graph Structure Learning with Dummy Nodes

[2:40] Lazy Estimation of Variable Importance for Large Neural Networks

(ends 2:45 PM)

3:30 p.m.

Poster Session 1 [3:30-5:30]

DNA: Domain Generalization with Diversified Neural Averaging

Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

DynaMixer: A Vision MLP Architecture with Dynamic Mixing

Channel Importance Matters in Few-Shot Image Classification

Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images

Certified Robustness Against Natural Language Attacks by Causal Intervention

A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

On the Learning of Non-Autoregressive Transformers

Latent Diffusion Energy-Based Model for Interpretable Text Modelling

UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

Black-Box Tuning for Language-Model-as-a-Service

Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information

Co-training Improves Prompt-based Learning for Large Language Models

Directed Acyclic Transformer for Non-Autoregressive Machine Translation

StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models

Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology

Generative Cooperative Networks for Natural Language Generation

What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?

Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding

Robust Group Synchronization via Quadratic Programming

UAST: Uncertainty-Aware Siamese Tracking

You Only Cut Once: Boosting Data Augmentation with a Single Cut

Generative Modeling for Multi-task Visual Learning

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

Parametric Visual Program Induction with Function Modularization

Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning

VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix

Neural Implicit Dictionary Learning via Mixture-of-Expert Training

Time Is MattEr: Temporal Self-supervision for Video Transformers

Benchmarking and Analyzing Point Cloud Classification under Corruptions

Understanding The Robustness in Vision Transformers

Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

On the Effects of Artificial Data Modification

Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective

Describing Differences between Text Distributions with Natural Language

Distinguishing rule- and exemplar-based generalization in learning systems

Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning

A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications

Minimizing Control for Credit Assignment with Strong Feedback

Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech

Towards Scaling Difference Target Propagation by Learning Backprop Targets

Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them

ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks

Provably Adversarially Robust Nearest Prototype Classifiers

Certifying Out-of-Domain Generalization for Blackbox Functions

Intriguing Properties of Input-Dependent Randomized Smoothing

To Smooth or Not? When Label Smoothing Meets Noisy Labels

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

On the Generalization Analysis of Adversarial Learning

Demystifying the Adversarial Robustness of Random Transformation Defenses

Double Sampling Randomized Smoothing

TPC: Transformation-Specific Smoothing for Point Cloud Models

Structural Entropy Guided Graph Hierarchical Pooling

Self-Supervised Representation Learning via Latent Graph Prediction

DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning

Analyzing and Mitigating Interference in Neural Architecture Search

Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees

Unified Scaling Laws for Routed Language Models

DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks

A deep convolutional neural network that is invariant to time rescaling

LyaNet: A Lyapunov Framework for Training Neural ODEs

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

On Collective Robustness of Bagging Against Data Poisoning

Hindering Adversarial Attacks with Implicit Neural Representations

From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling

ButterflyFlow: Building Invertible Layers with Butterfly Matrices

Controlling Conditional Language Models without Catastrophic Forgetting

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Structure-preserving GANs

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models

Equivariant Diffusion for Molecule Generation in 3D

Forward Operator Estimation in Generative Models with Kernel Transfer Operators

Conditional GANs with Auxiliary Discriminative Classifier

Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images

Matching Normalizing Flows and Probability Paths on Manifolds

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization

Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Region-Based Semantic Factorization in GANs

Online Continual Learning through Mutual Information Maximization

Learning Iterative Reasoning through Energy Minimization

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

PoF: Post-Training of Feature Extractor for Improving Generalization

Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation

Set Based Stochastic Subsampling

Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Generalizing to New Physical Systems via Context-Informed Dynamics Model

Self-conditioning Pre-Trained Language Models

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

pathGCN: Learning General Graph Spatial Operators from Paths

Graph-Coupled Oscillator Networks

HousE: Knowledge Graph Embedding with Householder Parameterization

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters

SpeqNets: Sparsity-aware permutation-equivariant graph networks

data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

Position Prediction as an Effective Pretraining Strategy

Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering

Deep and Flexible Graph Neural Architecture Search

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks

Large-Scale Graph Neural Architecture Search

Optimization-Induced Graph Implicit Nonlinear Diffusion

Prototype Based Classification from Hierarchy to Fairness

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

Deep Probability Estimation

Uncertainty Modeling in Generative Compressed Sensing

Going Deeper into Permutation-Sensitive Graph Neural Networks

Learning from Counterfactual Links for Link Prediction

Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations

Principal Component Flows

Bit Prioritization in Variational Autoencoders via Progressive Coding

Generative Flow Networks for Discrete Probabilistic Modeling

Diffusion bridges vector quantized variational autoencoders

Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization

Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

Modeling Irregular Time Series with Continuous Recurrent Units

TACTiS: Transformer-Attentional Copulas for Time Series

CerDEQ: Certifiable Deep Equilibrium Model

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics

IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data

GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing

Neural Laplace: Learning diverse classes of differential equations in the Laplace domain

Improving Language Models by Retrieving from Trillions of Tokens

Closed-Form Diffeomorphic Transformations for Time Series Alignment

Removing Batch Normalization Boosts Adversarial Training

Forget-free Continual Learning with Winning Subnetworks

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers

On the Practicality of Deterministic Epistemic Uncertainty

Combining Diverse Feature Priors

Multi-Task Learning as a Bargaining Game

Frustratingly Easy Transferability Estimation

Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

A Difference Standardization Method for Mutual Transfer Learning

Improving Task-free Continual Learning by Distributionally Robust Memory Evolution

A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity

Sparse Invariant Risk Minimization

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

A Closer Look at Smoothness in Domain Adversarial Training

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

Model Agnostic Sample Reweighting for Out-of-Distribution Learning

Zero-shot AutoML with Pretrained Models

Efficient Variance Reduction for Meta-learning

Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder

Partial disentanglement for domain adaptation

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data

Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Meta-Learning Hypothesis Spaces for Sequential Decision-making

A Tighter Analysis of Spectral Clustering, and Beyond

Online Active Regression

On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework

Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

Confidence Score for Source-Free Unsupervised Domain Adaptation

Gradient Based Clustering

Global Optimization of K-Center Clustering

Latent Outlier Exposure for Anomaly Detection with Contaminated Data

Coordinated Double Machine Learning

Exploiting Independent Instruments: Identification and Distribution Generalization

Partial Counterfactual Identification from Observational and Experimental Data

On Measuring Causal Contributions via do-interventions

The Role of Deconfounding in Meta-learning

CITRIS: Causal Identifiability from Temporal Intervened Sequences

Online Balanced Experimental Design

Minimum Cost Intervention Design for Causal Effect Identification

Causal structure-based root cause analysis of outliers

Instrumental Variable Regression with Confounder Balancing

Causal Transformer for Estimating Counterfactual Outcomes

Causal Inference Through the Structural Causal Marginal Problem

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

Matching Learned Causal Effects of Neural Networks with Domain Priors

Inferring Cause and Effect in the Presence of Heteroscedastic Noise

Exact Optimal Accelerated Complexity for Fixed-Point Iterations

Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions

NysADMM: faster composite convex optimization via low-rank approximation

FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding

Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems

Only tails matter: Average-Case Universality and Robustness in the Convex Regime

Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity

Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets

Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time

Active Sampling for Min-Max Fairness

Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

Stochastic Reweighted Gradient Descent

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

FedNL: Making Newton-Type Methods Applicable to Federated Learning

Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation

Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization

Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems

Probabilistic Bilevel Coreset Selection

Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs

On Implicit Bias in Overparameterized Bilevel Optimization

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective

Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering

Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(\epsilon^{-7/4})$ Complexity

Understanding the unstable convergence of gradient descent

Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm

FedNest: Federated Bilevel, Minimax, and Compositional Optimization

AdaGrad Avoids Saddle Points

Fast and Provable Nonconvex Tensor RPCA

On Convergence of Gradient Descent Ascent: A Tight Local Analysis

Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness

A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization

Anticorrelated Noise Injection for Improved Generalization

Tackling covariate shift with node-based Bayesian neural networks

Why the Rich Get Richer? On the Balancedness of Random Partition Models

A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation

Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems

Calibrated Learning to Defer with One-vs-All Classifiers

Tractable Uncertainty for Structure Learning

Path-Gradient Estimators for Continuous Normalizing Flows

Variational Feature Pyramid Networks

Additive Gaussian Processes Revisited

Probabilistic ODE Solutions in Millions of Dimensions

Adaptive Gaussian Process Change Point Detection

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

Fenrir: Physics-Enhanced Regression for Initial Value Problems

Variational nearest neighbor Gaussian process

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty

Bayesian Optimization under Stochastic Delayed Feedback

Bayesian Optimization for Distributionally Robust Chance-constrained Problem

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity

Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning

Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes

Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

Hessian-Free High-Resolution Nesterov Acceleration For Sampling

LSB: Local Self-Balancing MCMC in Discrete Spaces

A Langevin-like Sampler for Discrete Distributions

Scalable Spike-and-Slab

Nonparametric Involutive Markov Chain Monte Carlo

Continual Repeated Annealed Flow Transport Monte Carlo

Algorithms for the Communication of Samples

Low-Precision Stochastic Gradient Langevin Dynamics

Fast Relative Entropy Coding with A* coding

Accurate Quantization of Measures via Interacting Particle-based Optimization

Dynamic Regret of Online Markov Decision Processes

On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

Provable Reinforcement Learning with a Short-Term Memory

Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer

Mirror Learning: A Unifying Framework of Policy Optimisation

Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP

Learning Infinite-horizon Average-reward Markov Decision Process with Constraints

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

Langevin Monte Carlo for Contextual Bandits

Prompting Decision Transformer for Few-Shot Policy Generalization

Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning

Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

AnyMorph: Learning Transferable Polices By Inferring Agent Morphology

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer

Offline RL Policies Should Be Trained to be Adaptive

Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration

Supervised Off-Policy Ranking

The Primacy Bias in Deep Reinforcement Learning

Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

Model-Free Opponent Shaping

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning

Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

Self-Organized Polynomial-Time Coordination Graphs

Individual Reward Assisted Multi-Agent Reinforcement Learning

Generalized Beliefs for Cooperative AI

Greedy when Sure and Conservative when Uncertain about the Opponents

Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning

Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy

Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

Differentially Private Approximate Quantiles

Fairness Interventions as (Dis)Incentives for Strategic Manipulation

Robust Models Are More Interpretable Because Attributions Look Normal

Sequential Covariate Shift Detection Using Classifier Two-Sample Tests

A Joint Exponential Mechanism For Differentially Private Top-$k$

Transfer Learning In Differential Privacy's Hybrid-Model

Robust Kernel Density Estimation with Median-of-Means principle

Bounding Training Data Reconstruction in Private (Deep) Learning

Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks

FriendlyCore: Practical Differentially Private Aggregation

ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Public Data-Assisted Mirror Descent for Private Model Training

Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions

Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

Meaningfully debugging model mistakes using conceptual counterfactual explanations

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

Robust Counterfactual Explanations for Tree-Based Ensembles

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

Estimating and Penalizing Induced Preference Shifts in Recommender Systems

Framework for Evaluating Faithfulness of Local Explanations

A Consistent and Efficient Evaluation Strategy for Attribution Methods

Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four

Label-Descriptive Patterns and Their Application to Characterizing Classification Errors

XAI for Transformers: Better Explanations through Conservative Propagation

Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

Interpretable Off-Policy Learning via Hyperbox Search

Neuron Dependency Graphs: A Causal Abstraction of Neural Networks

On the Adversarial Robustness of Causal Algorithmic Recourse

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

POEM: Out-of-Distribution Detection with Posterior Sampling

Selective Network Linearization for Efficient Private Inference

Efficient Computation of Higher-Order Subgraph Attribution via Message Passing

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

Modular Conformal Calibration

Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

Context-Aware Drift Detection

Accelerating Shapley Explanation via Contributive Cooperator Selection

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

DAVINZ: Data Valuation using Deep Neural Networks at Initialization

Sample Efficient Learning of Predictors that Complement Humans

Online Learning for Min Sum Set Cover and Pandora’s Box

Smoothed Adversarial Linear Contextual Bandits with Knapsacks

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

Thompson Sampling for (Combinatorial) Pure Exploration

Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness

Rotting Infinitely Many-Armed Bandits

Batched Dueling Bandits

Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent

Consistent Polyhedral Surrogates for Top-k Classification and Variants

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback

Learning Mixtures of Linear Dynamical Systems

Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances

Residual-Based Sampling for Online Outlier-Robust PCA

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Streaming Algorithms for Support-Aware Histograms

Power-Law Escape Rate of SGD

Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model

Faster Algorithms for Learning Convex Functions

Feature selection using e-values

ActiveHedge: Hedge meets Active Learning

One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes

Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm

Robustness Implies Generalization via Data-Dependent Generalization Bounds

Learning to Hash Robustly, Guaranteed

Policy Gradient Method For Robust Reinforcement Learning

A query-optimal algorithm for finding counterfactuals

Linear Bandit Algorithms with Sublinear Time Complexity

Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra

Individual Preference Stability for Clustering

Correlated Quantization for Distributed Mean Estimation and Optimization

Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

The Algebraic Path Problem for Graph Metrics

Steerable 3D Spherical Neurons

H-Consistency Bounds for Surrogate Loss Minimizers

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent

The Teaching Dimension of Regularized Kernel Learners

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm

Multiclass learning with margin: exponential rates with no bias-variance trade-off

Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models

High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails

An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Topology-aware Generalization of Decentralized SGD

Understanding Gradient Descent on the Edge of Stability in Deep Learning

Cooperative Online Learning in Stochastic and Adversarial MDPs

Simple and near-optimal algorithms for hidden stratification and multi-group learning

Being Properly Improper

Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks

On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces

Minimax M-estimation under Adversarial Contamination

Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits

Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

Boosting Graph Structure Learning with Dummy Nodes

Lazy Estimation of Variable Importance for Large Neural Networks

(ends 5:30 PM)

4 p.m.

WED 20 JUL

3:30 a.m.

Breakfast on your own

4 a.m.

Registration Desk:

Registration Check-in Desk

(ends 4:00 PM)

6 a.m.

Invited Talk:

Solving the Right Problems: Making ML Models Relevant to Healthcare and the Life Sciences

Regina Barzilay

(ends 7:00 AM)

7 a.m.

Coffee Break

7:30 a.m.

Deep Learning [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Towards understanding how momentum improves generalization in deep learning

[7:35] What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?

[7:40] Deep equilibrium networks are sensitive to initialization statistics

[7:45] Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework

[7:50] Stability Based Generalization Bounds for Exponential Family Langevin Dynamics

[7:55] Local Augmentation for Graph Neural Networks

[8:00] On Non-local Convergence Analysis of Deep Linear Networks

Oral s 8:05-8:25

[8:05] Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum

Spotlight s 8:25-9:00

[8:25] Diversified Adversarial Attacks based on Conjugate Gradient Method

[8:30] On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features

[8:35] On the Equivalence Between Temporal and Static Equivariant Graph Representations

[8:40] Robust Training under Label Noise by Over-parameterization

[8:45] Implicit Bias of the Step Size in Linear Diagonal Neural Networks

[8:50] Extended Unconstrained Features Model for Exploring Deep Neural Collapse

[8:55] Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems

(ends 9:00 AM)

MISC: General Machine Learning Techniques [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Weisfeiler-Lehman Meets Gromov-Wasserstein

[7:35] GenLabel: Mixup Relabeling using Generative Models

[7:40] When and How Mixup Improves Calibration

[7:45] On Transportation of Mini-batches: A Hierarchical Approach

[7:50] VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty

[7:55] Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

[8:00] A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling

Oral s 8:05-8:25

[8:05] Stable Conformal Prediction Sets

Spotlight s 8:25-9:00

[8:25] Rethinking Fano’s Inequality in Ensemble Learning

[8:30] FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers

[8:35] Improving Mini-batch Optimal Transport via Partial Transportation

[8:40] Near-optimal rate of consistency for linear models with missing values

[8:45] Permutation Search of Tensor Network Structures via Local Sampling

[8:50] Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?

[8:55] DNNR: Differential Nearest Neighbors Regression

(ends 9:00 AM)

T: Learning Theory/Domain Adaptation [7:30-9:00]

Spotlight s 7:30-8:00

[7:30] Learning Domain Adaptive Object Detection with Probabilistic Teacher

[7:35] Adaptive Data Analysis with Correlated Observations

[7:40] Efficient PAC Learning from the Crowd with Pairwise Comparisons

[7:45] On the Statistical Benefits of Curriculum Learning

[7:50] Feature and Parameter Selection in Stochastic Linear Bandits

[7:55] Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

Oral s 8:00-8:20

[8:00] A new similarity measure for covariate shift with applications to nonparametric regression

Spotlight s 8:20-9:00

[8:20] Contextual Bandits with Large Action Spaces: Made Practical

[8:25] Identifiability Conditions for Domain Adaptation

[8:30] Streaming Algorithms for High-Dimensional Robust Statistics

[8:35] Popular decision tree algorithms are provably noise tolerant

[8:40] Understanding and Improving Knowledge Graph Embedding for Entity Alignment

[8:45] Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning

[8:50] Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees

[8:55] Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond

(ends 9:00 AM)

Applications [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

[7:35] One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams

[7:40] Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration

[7:45] ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases

[7:50] Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics

[7:55] Bayesian Imitation Learning for End-to-End Mobile Manipulation

[8:00] De novo mass spectrometry peptide sequencing with a transformer model

Oral s 8:05-8:25

[8:05] Learning inverse folding from millions of predicted structures

Spotlight s 8:25-9:00

[8:25] Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance

[8:30] MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection

[8:35] Proximal Exploration for Model-guided Protein Sequence Design

[8:40] Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval

[8:45] How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity

[8:50] Examining Scaling and Transfer of Language Model Architectures for Machine Translation

[8:55] State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks

(ends 9:00 AM)

PM: Variational Inference/Bayesian Models and Methods [7:30-9:00]

Oral s 7:30-7:50

[7:30] How Tempering Fixes Data Augmentation in Bayesian Neural Networks

Spotlight s 7:50-8:15

[7:50] Surrogate Likelihoods for Variational Annealed Importance Sampling

[7:55] Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes

[8:00] Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows

[8:05] Variational Sparse Coding with Learned Thresholding

[8:10] Structured Stochastic Gradient MCMC

Oral s 8:15-8:35

[8:15] BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression

Spotlight s 8:35-8:50

[8:35] Variational Inference with Locally Enhanced Bounds for Hierarchical Models

[8:40] Centroid Approximation for Bootstrap: Improving Particle Quality at Inference

[8:45] Deep Reference Priors: What is the best way to pretrain a model?

(ends 9:00 AM)

Reinforcement Learning: Deep RL [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Modeling Strong and Human-Like Gameplay with KL-Regularized Search

[7:35] Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

[7:40] Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning

[7:45] Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search

[7:50] Generalized Data Distribution Iteration

[7:55] Optimizing Tensor Network Contraction Using Reinforcement Learning

[8:00] History Compression via Language Models in Reinforcement Learning

Oral s 8:05-8:25

[8:05] REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer

Spotlight s 8:25-9:00

[8:25] LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation

[8:30] Efficient Learning for AlphaZero via Path Consistency

[8:35] A data-driven approach for learning to control computers

[8:40] Zero-Shot Reward Specification via Grounded Natural Language

[8:45] How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation

[8:50] Model-Value Inconsistency as a Signal for Epistemic Uncertainty

[8:55] Improving Policy Optimization with Generalist-Specialist Learning

(ends 9:00 AM)

DL: Theory [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] On Numerical Integration in Neural Ordinary Differential Equations

[7:35] Reverse Engineering the Neural Tangent Kernel

[7:40] Principled Knowledge Extrapolation with GANs

[7:45] Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity

[7:50] Data Augmentation as Feature Manipulation

[7:55] Convolutional and Residual Networks Provably Contain Lottery Tickets

[8:00] Feature Learning and Signal Propagation in Deep Neural Networks

Oral s 8:05-8:25

[8:05] Robust Training of Neural Networks Using Scale Invariant Architectures

Spotlight s 8:25-9:00

[8:25] Understanding Contrastive Learning Requires Incorporating Inductive Biases

[8:30] Implicit Regularization with Polynomial Growth in Deep Tensor Factorization

[8:35] Deep Network Approximation in Terms of Intrinsic Parameters

[8:40] Coin Flipping Neural Networks

[8:45] Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint

[8:50] More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize

[8:55] SE(3) Equivariant Graph Neural Networks with Complete Local Frames

(ends 9:00 AM)

Social Aspects/MISC [7:30-9:00]

Spotlight s 7:30-8:05

[7:30] Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings

[7:35] Label-Free Explainability for Unsupervised Models

[7:40] Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs

[7:45] A Study of Face Obfuscation in ImageNet

[7:50] Fair Representation Learning through Implicit Path Alignment

[7:55] Mitigating Neural Network Overconfidence with Logit Normalization

[8:00] Learning fair representation with a parametric integral probability metric

Oral s 8:05-8:25

[8:05] Privacy for Free: How does Dataset Condensation Help Privacy?

Spotlight s 8:25-9:00

[8:25] Fair Generalized Linear Models with a Convex Penalty

[8:30] HyperPrompt: Prompt-based Task-Conditioning of Transformers

[8:35] Validating Causal Inference Methods

[8:40] The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

[8:45] Scalable Deep Gaussian Markov Random Fields for General Graphs

[8:50] Anytime Information Cascade Popularity Prediction via Self-Exciting Processes

[8:55] Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

(ends 9:00 AM)

OPT: First Order [7:30-9:00]

Oral s 7:30-7:50

[7:30] Adapting to Mixing Time in Stochastic Optimization with Markovian Data

Spotlight s 7:50-8:15

[7:50] Fast Composite Optimization and Statistical Recovery in Federated Learning

[7:55] Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity

[8:00] Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

[8:05] Optimal Algorithms for Stochastic Multi-Level Compositional Optimization

[8:10] Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications

Oral s 8:15-8:35

[8:15] Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent

Spotlight s 8:35-9:00

[8:35] Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert

[8:40] ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

[8:45] Communication-Efficient Adaptive Federated Learning

[8:50] RECAPP: Crafting a More Efficient Catalyst for Convex Optimization

[8:55] Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots

(ends 9:00 AM)

T: Game Theory/RL/Planning [7:30-9:00]

Oral s 7:30-7:50

[7:30] A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes

Spotlight s 7:50-8:10

[7:50] The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces

[7:55] Extracting Latent State Representations with Linear Dynamics from Rich Observations

[8:00] For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria

[8:05] Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures

Oral s 8:10-8:30

[8:10] Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits

Spotlight s 8:30-8:55

[8:30] Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

[8:35] Learning to Infer Structures of Network Games

[8:40] Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation

[8:45] Near-Optimal Learning of Extensive-Form Games with Imperfect Information

[8:50] Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

(ends 9:00 AM)

8 a.m.

9 a.m.

Lunch Break On Your Own

10:15 a.m.

Deep Learning/APP:Computer Vision [10:15-11:45]

Spotlight s 10:15-10:50

[10:15] From data to functa: Your data point is a function and you can treat it like one

[10:20] DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

[10:25] Differentiable Top-k Classification Learning

[10:30] Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks

[10:35] Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks

[10:40] Training Your Sparse Neural Network Better with Any Mask

[10:45] Federated Learning with Positive and Unlabeled Data

Oral s 10:50-11:10

[10:50] Generating 3D Molecules for Target Protein Binding

Spotlight s 11:10-11:45

[11:10] Sparse Double Descent: Where Network Pruning Aggravates Overfitting

[11:15] Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs

[11:20] Revisiting Consistency Regularization for Deep Partial Label Learning

[11:25] Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification

[11:30] A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

[11:35] PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information

[11:40] Multicoated Supermasks Enhance Hidden Networks

(ends 11:45 AM)

Theory [10:15-11:45]

Spotlight s 10:15-10:50

[10:15] Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits

[10:20] On the Finite-Time Performance of the Knowledge Gradient Algorithm

[10:25] Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

[10:30] Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers

[10:35] No-Regret Learning in Time-Varying Zero-Sum Games

[10:40] Achieving Minimax Rates in Pool-Based Batch Active Learning

[10:45] Active Multi-Task Representation Learning

Oral s 10:50-11:10

[10:50] Active fairness auditing

Spotlight s 11:10-11:45

[11:10] Metric-Fair Active Learning

[11:15] Metric-Fair Classifier Derandomization

[11:20] Interactively Learning Preference Constraints in Linear Bandits

[11:25] Convergence of Uncertainty Sampling for Active Learning

[11:30] Thompson Sampling for Robust Transfer in Multi-Task Bandits

[11:35] Constants Matter: The Performance Gains of Active Learning

[11:40] Cross-Space Active Learning on Graph Convolutional Networks

(ends 11:45 AM)

APP: Chemistry and Drug Discovery [10:15-11:45]

Spotlight s 10:15-10:45

[10:15] MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution

[10:20] PINs: Progressive Implicit Networks for Multi-Scale Neural Representations

[10:25] Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

[10:30] Generative Coarse-Graining of Molecular Conformations

[10:35] LIMO: Latent Inceptionism for Targeted Molecule Generation

[10:40] Learning to Separate Voices by Spatial Regions

Oral s 10:45-11:05

[10:45] 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design

Spotlight s 11:05-11:40

[11:05] 3D Infomax improves GNNs for Molecular Property Prediction

[11:10] Biological Sequence Design with GFlowNets

[11:15] Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

[11:20] Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer

[11:25] Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense

[11:30] Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules

[11:35] EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

(ends 11:45 AM)

MISC: Representation Learning/Causality [10:15-11:45]

Spotlight s 10:15-10:45

[10:15] Decomposing Temporal High-Order Interactions via Latent ODEs

[10:20] Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets

[10:25] DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck

[10:30] End-to-End Balancing for Causal Continuous Treatment-Effect Estimation

[10:35] Role-based Multiplex Network Embedding

[10:40] Measure Estimation in the Barycentric Coding Model

Oral s 10:45-11:05

[10:45] RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Spotlight s 11:05-11:35

[11:05] Counterfactual Transportability: A Formal Approach

[11:10] Identification of Linear Non-Gaussian Latent Hierarchical Structure

[11:15] COAT: Measuring Object Compositionality in Emergent Representations

[11:20] Generalization and Robustness Implications in Object-Centric Learning

[11:25] NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

[11:30] Action-Sufficient State Representation Learning for Control with Structural Constraints

(ends 11:45 AM)

PM: Bayesian Models and Methods [10:15-11:45]

Oral s 10:15-10:35

[10:15] Bayesian Continuous-Time Tucker Decomposition

Spotlight s 10:35-11:00

[10:35] Approximate Bayesian Computation with Domain Expert in the Loop

[10:40] Discrete Probabilistic Inverse Optimal Transport

[10:45] Easy Variational Inference for Categorical Models via an Independent Binary Approximation

[10:50] Streaming Inference for Infinite Feature Models

[10:55] Optimizing Sequential Experimental Design with Deep Reinforcement Learning

Oral s 11:00-11:20

[11:00] Function-space Inference with Sparse Implicit Processes

Spotlight s 11:20-11:45

[11:20] Variational Inference for Infinitely Deep Neural Networks

[11:25] Personalized Federated Learning via Variational Bayesian Inference

[11:30] Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling

[11:35] Bayesian Deep Embedding Topic Meta-Learner

[11:40] Efficient Approximate Inference for Stationary Kernel on Frequency Domain

(ends 11:45 AM)

Reinforcement Learning [10:15-11:45]

Spotlight s 10:15-10:45

[10:15] Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning

[10:20] Analysis of Stochastic Processes through Replay Buffers

[10:25] Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning

[10:30] Communicating via Markov Decision Processes

[10:35] PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation

[10:40] DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning

Oral s 10:45-11:05

[10:45] Planning with Diffusion for Flexible Behavior Synthesis

Spotlight s 11:05-11:40

[11:05] A Temporal-Difference Approach to Policy Gradient Estimation

[11:10] MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

[11:15] Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency

[11:20] Actor-Critic based Improper Reinforcement Learning

[11:25] On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs

[11:30] The Geometry of Robust Value Functions

[11:35] Denoised MDPs: Learning World Models Better Than the World Itself

(ends 11:45 AM)

SA: Trustworthy Machine Learning [10:15-11:45]

Oral s 10:15-10:35

[10:15] Tight and Robust Private Mean Estimation with Few Users

Spotlight s 10:35-11:00

[10:35] QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning

[10:40] Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

[10:45] Sanity Simulations for Saliency Methods

[10:50] Out-of-Distribution Detection with Deep Nearest Neighbors

[10:55] Differentially Private Maximal Information Coefficients

Oral s 11:00-11:20

[11:00] Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data

Spotlight s 11:20-11:45

[11:20] On the Difficulty of Defending Self-Supervised Learning against Model Extraction

[11:25] Adversarial Attack and Defense for Non-Parametric Two-Sample Tests

[11:30] Certified Adversarial Robustness Under the Bounded Support Set

[11:35] Predicting Out-of-Distribution Error with the Projection Norm

[11:40] Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization

(ends 11:45 AM)

DL: Robustness [10:15-11:45]

Spotlight s 10:15-10:50

[10:15] Generating Distributional Adversarial Examples to Evade Statistical Detectors

[10:20] Improving Out-of-Distribution Robustness via Selective Augmentation

[10:25] Modeling Adversarial Noise for Adversarial Training

[10:30] Improving Adversarial Robustness via Mutual Information Estimation

[10:35] FOCUS: Familiar Objects in Common and Uncommon Settings

[10:40] Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization

[10:45] Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing

Oral s 10:50-11:10

[10:50] A Dynamical System Perspective for Lipschitz Neural Networks

Spotlight s 11:10-11:45

[11:10] Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)

[11:15] Neurotoxin: Durable Backdoors in Federated Learning

[11:20] Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

[11:25] Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching

[11:30] Fast Lossless Neural Compression with Integer-Only Discrete Flows

[11:35] SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

[11:40] SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation

(ends 11:45 AM)

T: Online Learning and Bandits/Learning Theory [10:15-11:45]

Oral s 10:15-10:35

[10:15] Generative Trees: Adversarial and Copycat

Spotlight s 10:35-11:00

[10:35] A Resilient Distributed Boosting Algorithm

[10:40] Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards

[10:45] On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation

[10:50] Congested Bandits: Optimal Routing via Short-term Resets

[10:55] Stochastic Rising Bandits

Oral s 11:00-11:20

[11:00] Agnostic Learnability of Halfspaces via Logistic Loss

Spotlight s 11:20-11:45

[11:20] Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

[11:25] PDE-Based Optimal Strategy for Unconstrained Online Learning

[11:30] Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out

[11:35] On Learning Mixture of Linear Regressions in the Non-Realizable Setting

[11:40] Random Forest Density Estimation

(ends 11:45 AM)

DL: Robustness [10:15-11:45]

Spotlight s 10:15-10:50

[10:15] DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning

[10:20] Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

[10:25] RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval

[10:30] Modeling Structure with Undirected Neural Networks

[10:35] Certified Neural Network Watermarks with Randomized Smoothing

[10:40] Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation

[10:45] Adversarial Vulnerability of Randomized Ensembles

Oral s 10:50-11:10

[10:50] Robustness Verification for Contrastive Learning

Spotlight s 11:10-11:45

[11:10] The CLRS Algorithmic Reasoning Benchmark

[11:15] Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

[11:20] Understanding Robust Generalization in Learning Regular Languages

[11:25] Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification

[11:30] AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

[11:35] A Modern Self-Referential Weight Matrix That Learns to Modify Itself

[11:40] Short-Term Plasticity Neurons Learning to Learn and Forget

(ends 11:45 AM)

11:45 a.m.

Coffee Break

12:15 p.m.

Invited Talk:

Synthetic Control Methods and Difference-In-Differences

Guido Imbens

(ends 1:15 PM)

1 p.m.

1:15 p.m.

Short Break

1:30 p.m.

Deep Learning [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] $p$-Laplacian Based Graph Neural Networks

[1:35] Equivariant Quantum Graph Circuits

[1:40] A Theoretical Comparison of Graph Neural Network Extensions

[1:45] Variational On-the-Fly Personalization

[1:50] Deep symbolic regression for recurrence prediction

[1:55] Geometric Multimodal Contrastive Representation Learning

[2:00] Universality of Winning Tickets: A Renormalization Group Perspective

Oral s 2:05-2:25

[2:05] Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition

Spotlight s 2:25-3:00

[2:25] Loss Function Learning for Domain Generalization by Implicit Gradient

[2:30] GraphFM: Improving Large-Scale GNN Training via Feature Momentum

[2:35] Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

[2:40] A Differential Entropy Estimator for Training Neural Networks

[2:45] Scaling Out-of-Distribution Detection for Real-World Settings

[2:50] Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

[2:55] SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

(ends 3:00 PM)

Theory [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] The dynamics of representation learning in shallow, non-linear autoencoders

[1:35] Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

[1:40] Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing

[1:45] Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

[1:50] Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation

[1:55] Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows

[2:00] Bounding the Width of Neural Networks via Coupled Initialization - A Worst Case Analysis

Oral s 2:05-2:25

[2:05] Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

Spotlight s 2:25-3:00

[2:25] The Neural Race Reduction: Dynamics of Abstraction in Gated Networks

[2:30] Efficient Learning of CNNs using Patch Based Features

[2:35] Neural Tangent Kernel Analysis of Deep Narrow Neural Networks

[2:40] Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

[2:45] Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis

[2:50] Non-Vacuous Generalisation Bounds for Shallow Neural Networks

[2:55] Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation

(ends 3:00 PM)

Applications [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

[1:35] Matching Structure for Dual Learning

[1:40] BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

[1:45] YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone

[1:50] Inducing Causal Structure for Interpretable Neural Networks

[1:55] SDQ: Stochastic Differentiable Quantization with Mixed Precision

[2:00] IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Oral s 2:05-2:25

[2:05] Re-evaluating Word Mover's Distance

Spotlight s 2:25-3:00

[2:25] Translatotron 2: High-quality direct speech-to-speech translation with voice preservation

[2:30] Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation

[2:35] Symmetric Machine Theory of Mind

[2:40] PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance

[2:45] LCANets: Lateral Competition Improves Robustness Against Corruption and Attack

[2:50] Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

[2:55] Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps

(ends 3:00 PM)

Reinforcement Learning [1:30-3:05]

Spotlight s 1:30-2:05

[1:30] Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning

[1:35] Bayesian Nonparametrics for Offline Skill Discovery

[1:40] Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime

[1:45] Curriculum Reinforcement Learning via Constrained Optimal Transport

[1:50] Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs

[1:55] Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning

[2:00] Constrained Offline Policy Optimization

Oral s 2:05-2:25

[2:05] Causal Dynamics Learning for Task-Independent State Abstraction

Spotlight s 2:25-3:05

[2:25] Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

[2:30] Reinforcement Learning with Action-Free Pre-Training from Videos

[2:35] Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

[2:40] Delayed Reinforcement Learning by Imitation

[2:45] Reachability Constrained Reinforcement Learning

[2:50] Adaptive Model Design for Markov Decision Process

[2:55] Goal Misgeneralization in Deep Reinforcement Learning

[3:00] Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots

(ends 3:05 PM)

DL: Algorithms [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] The Infinite Contextual Graph Markov Model

[1:35] RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

[1:40] Detached Error Feedback for Distributed SGD with Random Sparsification

[1:45] Training OOD Detectors in their Natural Habitats

[1:50] Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks

[1:55] Neural Tangent Kernel Empowered Federated Learning

[2:00] Probabilistically Robust Learning: Balancing Average- and Worst-case Performance

Oral s 2:05-2:25

[2:05] Adversarially trained neural representations are already as robust as biological neural representations

Spotlight s 2:25-3:00

[2:25] Feature Space Particle Inference for Neural Network Ensembles

[2:30] A Study on the Ramanujan Graph Property of Winning Lottery Tickets

[2:35] PAC-Net: A Model Pruning Approach to Inductive Transfer Learning

[2:40] EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning

[2:45] Fisher SAM: Information Geometry and Sharpness Aware Minimisation

[2:50] Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry

[2:55] Towards Understanding Sharpness-Aware Minimization

(ends 3:00 PM)

SA: Privacy-preserving Statistics and Machine Learning [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] Improved Regret for Differentially Private Exploration in Linear MDP

[1:35] Differentially Private Community Detection for Stochastic Block Models

[1:40] Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

[1:45] Hermite Polynomial Features for Private Data Generation

[1:50] How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection

[1:55] Deduplicating Training Data Mitigates Privacy Risks in Language Models

[2:00] Private frequency estimation via projective geometry

Oral s 2:05-2:25

[2:05] The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation

Spotlight s 2:25-3:00

[2:25] Faster Privacy Accounting via Evolving Discretization

[2:30] The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning

[2:35] Private Adaptive Optimization with Side information

[2:40] Secure Quantized Training for Deep Learning

[2:45] Private optimization in the interpolation regime: faster rates and hardness results

[2:50] Differentially Private Coordinate Descent for Composite Empirical Risk Minimization

[2:55] Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making

(ends 3:00 PM)

Deep Learning/Optimization [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization

[1:35] Implicit Bias of Linear Equivariant Networks

[1:40] The State of Sparse Training in Deep Reinforcement Learning

[1:45] Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets

[1:50] Datamodels: Understanding Predictions with Data and Data with Predictions

[1:55] Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

[2:00] Deep Causal Metric Learning

Oral s 2:05-2:25

[2:05] Not All Poisons are Created Equal: Robust Training against Data Poisoning

Spotlight s 2:25-3:00

[2:25] Learning Symmetric Embeddings for Equivariant World Models

[2:30] Accelerated Federated Learning with Decoupled Adaptive Optimization

[2:35] Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

[2:40] TSPipe: Learn from Teacher Faster with Pipelines

[2:45] Personalized Federated Learning through Local Memorization

[2:50] Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points

[2:55] Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training

(ends 3:00 PM)

Miscellaneous Aspects of Machine Learning/Reinforcement Learning [1:30-3:00]

Spotlight s 1:30-2:05

[1:30] Gradient Descent on Neurons and its Link to Approximate Second-order Optimization

[1:35] A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources

[1:40] Efficient Online ML API Selection for Multi-Label Classification Tasks

[1:45] Entropic Causal Inference: Graph Identifiability

[1:50] Architecture Agnostic Federated Learning for Neural Networks

[1:55] Conformal Prediction Sets with Limited False Positives

[2:00] Scalable Computation of Causal Bounds

Oral s 2:05-2:25

[2:05] LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood

Spotlight s 2:25-3:00

[2:25] Learning Pseudometric-based Action Representations for Offline Reinforcement Learning

[2:30] A Statistical Manifold Framework for Point Cloud Data

[2:35] HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

[2:40] A Natural Actor-Critic Framework for Zero-Sum Markov Games

[2:45] Distributionally Robust $Q$-Learning

[2:50] Sparsity in Partially Controllable Linear Systems

[2:55] Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation

(ends 3:00 PM)

Deep Learning/Optimization [1:30-3:00]

Spotlight s 1:30-2:00

[1:30] NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks

[1:35] Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm

[1:40] Auxiliary Learning with Joint Task and Data Scheduling

[1:45] Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence

[1:50] Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers

[1:55] Generalizing Gaussian Smoothing for Random Search

Oral s 2:00-2:20

[2:00] A General Recipe for Likelihood-free Bayesian Optimization

Spotlight s 2:20-2:55

[2:20] Constrained Discrete Black-Box Optimization using Mixed-Integer Programming

[2:25] Risk-Averse No-Regret Learning in Online Convex Games

[2:30] Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters

[2:35] Robust Multi-Objective Bayesian Optimization Under Input Noise

[2:40] Gradient-Free Method for Heavily Constrained Nonconvex Optimization

[2:45] Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound

[2:50] The power of first-order smooth optimization for black-box non-smooth problems

(ends 3:00 PM)

Deep Learning/MISC [1:30-3:00]

Spotlight s 1:30-2:00

[1:30] A New Perspective on the Effects of Spectrum in Graph Neural Networks

[1:35] Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

[1:40] Partial Label Learning via Label Influence Function

[1:45] Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees

[1:50] Understanding Robust Overfitting of Adversarial Training and Beyond

[1:55] A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources

Oral s 2:00-2:20

[2:00] Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.

Spotlight s 2:20-2:55

[2:20] Supervised Learning with General Risk Functionals

[2:25] Locally Sparse Neural Networks for Tabular Biomedical Data

[2:30] Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification

[2:35] Detecting Corrupted Labels Without Training a Model to Predict

[2:40] Prototype-Anchored Learning for Learning with Imperfect Annotations

[2:45] Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters

[2:50] Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm

(ends 3:00 PM)

3:30 p.m.

Poster Session 2 [3:30-5:30]