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SUN 17 JUL

6 a.m.

7 a.m.

(ends 4:00 PM)

9 a.m.

9:30 a.m.

Expo Talk Panel:

(ends 10:15 AM)

10:20 a.m.

Expo Demonstration:

(ends 11:20 AM)

11 a.m.

Cancelled:

(ends 11:45 AM)

11:30 a.m.

Coffee Break

12:15 p.m.

Expo Demonstration:

(ends 1:00 PM)

1:15 p.m.

2:30 p.m.

MON 18 JUL

4 a.m.

(ends 3:00 PM)

5 a.m.

5:30 a.m.

6 a.m.

6:30 a.m.

Tutorial:

(ends 8:45 AM)

7 a.m.

Coffee Break

8 a.m.

9 a.m.

Lunch Break - on your own

10 a.m.

Tutorial:

(ends 12:00 PM)

11 a.m.

noon

Coffee Break

12:30 p.m.

Tutorial:

(ends 2:50 PM)

4 p.m.

TUE 19 JUL

3:30 a.m.

Breakfast on your own

4 a.m.

(ends 4:00 PM)

5:45 a.m.

6 a.m.

7 a.m.

Coffee Break

7:30 a.m.

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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)

Orals 7:30-7:50

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

Spotlights 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

Orals 8:10-8:30

[8:10]
Tractable Uncertainty for Structure Learning

Spotlights 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)

Spotlights 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

Orals 8:00-8:20

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

Spotlights 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)

Orals 7:30-7:50

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

Spotlights 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

Orals 8:10-8:30

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

Spotlights 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)

Spotlights 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

Orals 8:00-8:20

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

Spotlights 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)

Orals 7:30-7:50

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

Spotlights 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

Orals 8:15-8:35

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

Spotlights 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)

Orals 7:30-7:50

[7:30]
Online Learning for Min Sum Set Cover and Pandoraâ€™s Box

Spotlights 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

Orals 8:15-8:35

[8:15]
Batched Dueling Bandits

Spotlights 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)

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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)

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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)

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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.

Spotlights 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

Orals 11:00-11:20

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

Spotlights 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)

Spotlights 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

Orals 11:05-11:25

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

Spotlights 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)

Spotlights 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

Orals 11:00-11:20

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

Spotlights 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)

Orals 10:30-10:50

[10:30]
Learning Mixtures of Linear Dynamical Systems

Spotlights 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

Orals 11:15-11:35

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

Spotlights 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)

Spotlights 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

Orals 11:00-11:20

[11:00]
Online Active Regression

Spotlights 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)

Spotlights 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

Orals 11:00-11:20

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

Spotlights 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)

Orals 10:30-10:50

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

Spotlights 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

Orals 11:15-11:35

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

Spotlights 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)

Orals 10:30-10:50

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

Spotlights 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

Orals 11:10-11:30

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

Spotlights 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)

Spotlights 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

Orals 11:05-11:25

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

Spotlights 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)

Orals 10:30-10:50

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

Spotlights 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

Orals 11:15-11:35

[11:15]
Individual Preference Stability for Clustering

Spotlights 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.

1 p.m.

Short Break

1:15 p.m.

Spotlights 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

Orals 1:50-2:10

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

Spotlights 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)

Spotlights 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

Orals 1:50-2:10

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

Spotlights 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)

Orals 1:15-1:35

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

Spotlights 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

Orals 1:55-2:15

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

Spotlights 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)

Orals 1:15-1:35

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

Spotlights 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

Orals 2:00-2:20

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

Spotlights 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)

Spotlights 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

Orals 1:45-2:05

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

Spotlights 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)

Orals 1:15-1:35

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

Spotlights 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

Orals 2:00-2:20

[2:00]
Nonparametric Involutive Markov Chain Monte Carlo

Spotlights 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)

Spotlights 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

Orals 1:45-2:05

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

Spotlights 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)

Spotlights 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

Orals 1:45-2:05

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

Spotlights 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)

Spotlights 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

Orals 1:45-2:05

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

Spotlights 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)

Orals 1:15-1:35

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

Spotlights 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

Orals 2:00-2:20

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

Spotlights 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.

Posters 3:30-5:30

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

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

(ends 5:30 PM)

4 p.m.

WED 20 JUL

3:30 a.m.

Breakfast on your own

4 a.m.

(ends 4:00 PM)

6 a.m.

Invited Talk:

Regina Barzilay

(ends 7:00 AM)

7 a.m.

Coffee Break

7:30 a.m.

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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)

Spotlights 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

Orals 8:05-8:25

[8:05]
Stable Conformal Prediction Sets

Spotlights 8:25-9:00

[8:25]
Rethinking Fanoâ€™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)

Spotlights 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

Orals 8:00-8:20

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

Spotlights 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)

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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)

Orals 7:30-7:50

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

Spotlights 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]
Fatâ€“Tailed Variational Inference with Anisotropic Tail Adaptive Flows

[8:05]
Variational Sparse Coding with Learned Thresholding

[8:10]
Structured Stochastic Gradient MCMC

Orals 8:15-8:35

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

Spotlights 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)

Spotlights 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

Orals 8:05-8:25

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

Spotlights 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)

Spotlights 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