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SUN 18 JUL
5 a.m.
Expo Talk Panel:
Machine Learning for Addressing Power Grid Congestion
(ends 6:00 AM)
Expo Workshop:
Real World RL: Azure Personalizer & Vowpal Wabbit
(ends 9:30 AM)
Expo Workshop:
High-Quality Data Labeling at Scale with Toloka
(ends 9:00 AM)
6 a.m.
Expo Talk Panel:
The latest advances and use-cases in the field of Causal AI
(ends 7:00 AM)
7 a.m.
Expo Talk Panel:
Unique Research Opportunities in AI Algorithms, Health, Traffic, and Weather
(ends 8:00 AM)
8 a.m.
Expo Talk Panel:
Cool Moves: Decoupling physical motions and self-avatar motions in Virtual Reality
(ends 9:00 AM)
9 a.m.
Expo Demonstration:
Neural Video Codec Demo
(ends 10:00 AM)
10 a.m.
Expo Talk Panel:
IBM Analog Hardware Acceleration Kit
(ends 11:00 AM)
5 p.m.
Expo Talk Panel:
Enterprise-Strength Federated Learning: New Algorithms, New Paradigms, and a Participant-Interactive Demonstration Session
(ends 5:59 PM)
Expo Workshop:
PaddlePaddle-based Deep Learning at Baidu
(ends 9:00 PM)
7 p.m.
Expo Talk Panel:
Graviti Open Datasets: A peek into the future of Open Data
(ends 8:00 PM)
9 p.m.
Expo Demonstration:
Explainable Entity Matching for Master Data Management
(ends 10:00 PM)
MON 19 JUL
6 a.m.
8 a.m.
Tutorial:
Continual Learning with Deep Architectures
(ends 11:05 AM)
Tutorial:
From ML research to ML products: A path towards building models with real-world impact
(ends 11:15 AM)
Tutorial:
Natural-XAI: Explainable AI with Natural Language Explanations
(ends 11:00 AM)
Tutorial:
Responsible AI in Industry: Practical Challenges and Lessons Learned
(ends 11:00 AM)
Tutorial:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training
(ends 10:59 AM)
Tutorial:
Synthetic Healthcare Data Generation and Assessment: Challenges, Methods, and Impact on Machine Learning
(ends 11:00 AM)
Affinity Workshop:
LatinX in AI (LXAI) Research at ICML 2021
(ends 5:00 PM)
noon
Tutorial:
Online and non-stochastic control
(ends 3:00 PM)
Tutorial:
Random Matrix Theory and ML (RMT+ML)
(ends 3:00 PM)
Tutorial:
Unsupervised Learning for Reinforcement Learning
(ends 3:00 PM)
Tutorial:
Social Implications of Large Language Models
(ends 2:50 PM)
5 p.m.
6 p.m.
Affinity Poster Session:
LatinX in AI, Queer in AI, WiML - Joint Poster Session
(ends 8:00 PM)
8 p.m.
Tutorial:
Privacy in learning: Basics and the interplay
(ends 11:00 PM)
Tutorial:
Self-Attention for Computer Vision
(ends 10:45 PM)
TUE 20 JUL
5 a.m.
Auto-ML and Optimization [5:00-6:00]
Oral s 5:00-5:20
[5:00] BORE: Bayesian Optimization by Density-Ratio Estimation
Spotlight s 5:20-5:45
[5:20] AutoSampling: Search for Effective Data Sampling Schedules
[5:25] HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
[5:30] Bias-Robust Bayesian Optimization via Dueling Bandits
[5:35] Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
[5:40] Sparsifying Networks via Subdifferential Inclusion
Q&A s 5:45-5:50
[5:45] Q&A
(ends 6:00 AM)
Deep Learning Architectures [5:00-6:00]
Oral s 5:00-5:20
[5:00] Relative Positional Encoding for Transformers with Linear Complexity
Spotlight s 5:20-5:50
[5:20] A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
[5:25] A Unified Lottery Ticket Hypothesis for Graph Neural Networks
[5:30] Generative Adversarial Transformers
[5:35] Evolving Attention with Residual Convolutions
[5:40] Zoo-Tuning: Adaptive Transfer from A Zoo of Models
[5:45] UnICORNN: A recurrent model for learning very long time dependencies
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Deep Learning Applications [5:00-6:00]
Oral s 5:00-5:20
[5:00] Attention is not all you need: pure attention loses rank doubly exponentially with depth
Spotlight s 5:20-5:50
[5:20] Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
[5:25] Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model
[5:30] Exploiting structured data for learning contagious diseases under incomplete testing
[5:35] Strategic Classification Made Practical
[5:40] Large-Margin Contrastive Learning with Distance Polarization Regularizer
[5:45] SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Graph Learning [5:00-6:00]
Oral s 5:00-5:20
[5:00] Size-Invariant Graph Representations for Graph Classification Extrapolations
Spotlight s 5:20-5:50
[5:20] Consistent Nonparametric Methods for Network Assisted Covariate Estimation
[5:25] Explainable Automated Graph Representation Learning with Hyperparameter Importance
[5:30] Breaking the Limits of Message Passing Graph Neural Networks
[5:35] From Local Structures to Size Generalization in Graph Neural Networks
[5:40] Interpretable Stability Bounds for Spectral Graph Filters
[5:45] Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Reinforcement Learning (Multi-agent) [5:00-6:00]
Oral s 5:00-5:20
[5:00] Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Spotlight s 5:20-5:45
[5:20] UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
[5:25] A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
[5:30] Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
[5:35] PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
[5:40] Imitation by Predicting Observations
Q&A s 5:45-5:50
[5:45] Q&A
(ends 6:00 AM)
Optimal Transport [5:00-6:00]
Oral s 5:00-5:20
[5:00] Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Spotlight s 5:20-5:50
[5:20] Outlier-Robust Optimal Transport
[5:25] Dataset Dynamics via Gradient Flows in Probability Space
[5:30] Sliced Iterative Normalizing Flows
[5:35] Low-Rank Sinkhorn Factorization
[5:40] Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
[5:45] Making transport more robust and interpretable by moving data through a small number of anchor points
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Optimization (Distributed) [5:00-6:00]
Oral s 5:00-5:20
[5:00] Optimal Complexity in Decentralized Training
Spotlight s 5:20-5:50
[5:20] Stochastic Sign Descent Methods: New Algorithms and Better Theory
[5:25] Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
[5:30] A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
[5:35] Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction
[5:40] Newton Method over Networks is Fast up to the Statistical Precision
[5:45] Federated Learning under Arbitrary Communication Patterns
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Deep Reinforcement Learning 1 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Phasic Policy Gradient
Spotlight s 5:20-5:50
[5:20] Reinforcement Learning with Prototypical Representations
[5:25] Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
[5:30] Muesli: Combining Improvements in Policy Optimization
[5:35] Unsupervised Learning of Visual 3D Keypoints for Control
[5:40] Learning Task Informed Abstractions
[5:45] State Entropy Maximization with Random Encoders for Efficient Exploration
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Deep Reinforcement Learning 2 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Deeply-Debiased Off-Policy Interval Estimation
Spotlight s 5:20-5:45
[5:20] Offline Contextual Bandits with Overparameterized Models
[5:25] Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation
[5:30] A New Representation of Successor Features for Transfer across Dissimilar Environments
[5:35] Preferential Temporal Difference Learning
[5:40] On the Optimality of Batch Policy Optimization Algorithms
Q&A s 5:45-5:50
[5:45] Q&A
(ends 6:00 AM)
6 a.m.
AutoML and Deep Architecture [6:00-7:00]
Oral s 6:00-6:20
[6:00] Neural Architecture Search without Training
Spotlight s 6:20-6:50
[6:20] Is Space-Time Attention All You Need for Video Understanding?
[6:25] A Probabilistic Approach to Neural Network Pruning
[6:30] KNAS: Green Neural Architecture Search
[6:35] Efficient Lottery Ticket Finding: Less Data is More
[6:40] ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
[6:45] Provably Strict Generalisation Benefit for Equivariant Models
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Deep Learning Algorithms 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Leveraging Sparse Linear Layers for Debuggable Deep Networks
Spotlight s 6:20-6:50
[6:20] Voice2Series: Reprogramming Acoustic Models for Time Series Classification
[6:25] Self-Tuning for Data-Efficient Deep Learning
[6:30] How Framelets Enhance Graph Neural Networks
[6:35] Federated Continual Learning with Weighted Inter-client Transfer
[6:40] Self Normalizing Flows
[6:45] Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Deep Learning (Bayesian) [6:00-7:00]
Oral s 6:00-6:20
[6:00] What Are Bayesian Neural Network Posteriors Really Like?
Spotlight s 6:20-6:50
[6:20] Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
[6:25] Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
[6:30] Deep kernel processes
[6:35] Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
[6:40] Bayesian Deep Learning via Subnetwork Inference
[6:45] Generative Particle Variational Inference via Estimation of Functional Gradients
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Deep Learning Algorithms 2 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Principled Simplicial Neural Networks for Trajectory Prediction
Spotlight s 6:20-6:50
[6:20] Efficient Differentiable Simulation of Articulated Bodies
[6:25] On Monotonic Linear Interpolation of Neural Network Parameters
[6:30] Connecting Sphere Manifolds Hierarchically for Regularization
[6:35] Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
[6:40] Thinking Like Transformers
[6:45] Federated Learning of User Verification Models Without Sharing Embeddings
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Deep Generative Model 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Spotlight s 6:20-6:50
[6:20] Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
[6:25] GraphDF: A Discrete Flow Model for Molecular Graph Generation
[6:30] Hierarchical VAEs Know What They Don’t Know
[6:35] Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
[6:40] Generative Video Transformer: Can Objects be the Words?
[6:45] Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Deep Learning Theory 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
Spotlight s 6:20-6:50
[6:20] Fundamental Tradeoffs in Distributionally Adversarial Training
[6:25] Towards Understanding Learning in Neural Networks with Linear Teachers
[6:30] Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
[6:35] A Functional Perspective on Learning Symmetric Functions with Neural Networks
[6:40] Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
[6:45] On the Random Conjugate Kernel and Neural Tangent Kernel
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Optimization 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Spotlight s 6:20-6:50
[6:20] Projection Robust Wasserstein Barycenters
[6:25] Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams
[6:30] Distributionally Robust Optimization with Markovian Data
[6:35] Acceleration via Fractal Learning Rate Schedules
[6:40] A Novel Sequential Coreset Method for Gradient Descent Algorithms
[6:45] Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Optimization (Convex) 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Spotlight s 6:20-6:50
[6:20] Dueling Convex Optimization
[6:25] Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
[6:30] Parameter-free Locally Accelerated Conditional Gradients
[6:35] Principal Component Hierarchy for Sparse Quadratic Programs
[6:40] One-sided Frank-Wolfe algorithms for saddle problems
[6:45] ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Reinforcement Learning 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
Spotlight s 6:20-6:50
[6:20] Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
[6:25] Neuro-algorithmic Policies Enable Fast Combinatorial Generalization
[6:30] PID Accelerated Value Iteration Algorithm
[6:35] Provably Efficient Learning of Transferable Rewards
[6:40] Reinforcement Learning for Cost-Aware Markov Decision Processes
[6:45] Value Alignment Verification
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
7 a.m.
AutoML and Neural Network Architectures 1 [7:00-8:00]
Oral s 7:00-7:20
[7:00] OmniNet: Omnidirectional Representations from Transformers
Spotlight s 7:20-7:45
[7:20] Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
[7:25] E(n) Equivariant Graph Neural Networks
[7:30] Grid-Functioned Neural Networks
[7:35] MSA Transformer
[7:40] Parallelizing Legendre Memory Unit Training
Q&A s 7:45-7:50
[7:45] Q&A
(ends 8:00 AM)
Deep Learning Algorithms 3 [7:00-8:00]
Oral s 7:00-7:20
[7:00] ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Spotlight s 7:20-7:50
[7:20] Learning Curves for Analysis of Deep Networks
[7:25] GLSearch: Maximum Common Subgraph Detection via Learning to Search
[7:30] Learning Intra-Batch Connections for Deep Metric Learning
[7:35] Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
[7:40] Unifying Vision-and-Language Tasks via Text Generation
[7:45] DeepWalking Backwards: From Embeddings Back to Graphs
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Deep Generative Model 2 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders
Spotlight s 7:20-7:50
[7:20] Riemannian Convex Potential Maps
[7:25] Autoencoding Under Normalization Constraints
[7:30] PixelTransformer: Sample Conditioned Signal Generation
[7:35] Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
[7:40] Autoencoder Image Interpolation by Shaping the Latent Space
[7:45] Improved Denoising Diffusion Probabilistic Models
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Deep Learning Algorithms 4 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Directional Graph Networks
Spotlight s 7:20-7:50
[7:20] Winograd Algorithm for AdderNet
[7:25] LieTransformer: Equivariant Self-Attention for Lie Groups
[7:30] "Hey, that's not an ODE": Faster ODE Adjoints via Seminorms
[7:35] Graph Mixture Density Networks
[7:40] Momentum Residual Neural Networks
[7:45] Better Training using Weight-Constrained Stochastic Dynamics
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Reinforcement Learning 2 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition
Spotlight s 7:20-7:50
[7:20] Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
[7:25] A New Formalism, Method and Open Issues for Zero-Shot Coordination
[7:30] Targeted Data Acquisition for Evolving Negotiation Agents
[7:35] Inverse Constrained Reinforcement Learning
[7:40] Counterfactual Credit Assignment in Model-Free Reinforcement Learning
[7:45] Interactive Learning from Activity Description
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Optimization 2 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
Spotlight s 7:20-7:50
[7:20] Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
[7:25] Variational Data Assimilation with a Learned Inverse Observation Operator
[7:30] Fast Projection Onto Convex Smooth Constraints
[7:35] Decomposable Submodular Function Minimization via Maximum Flow
[7:40] Multiplicative Noise and Heavy Tails in Stochastic Optimization
[7:45] Distributed Second Order Methods with Fast Rates and Compressed Communication
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Deep Learning 1 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Not All Memories are Created Equal: Learning to Forget by Expiring
Spotlight s 7:20-7:50
[7:20] Learning Bounds for Open-Set Learning
[7:25] Perceiver: General Perception with Iterative Attention
[7:30] Synthesizer: Rethinking Self-Attention for Transformer Models
[7:35] Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks
[7:40] What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
[7:45] Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Reinforcement Learning and Planning 1 [7:00-8:00]
Oral s 7:00-7:20
[7:00] World Model as a Graph: Learning Latent Landmarks for Planning
Spotlight s 7:20-7:45
[7:20] Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
[7:25] Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
[7:30] Offline Reinforcement Learning with Pseudometric Learning
[7:35] EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
[7:40] Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond
Q&A s 7:45-7:50
[7:45] Q&A
(ends 8:00 AM)
Reinforcement Learning and Planning 2 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Skill Discovery for Exploration and Planning using Deep Skill Graphs
Spotlight s 7:20-7:50
[7:20] Learning Routines for Effective Off-Policy Reinforcement Learning
[7:25] PODS: Policy Optimization via Differentiable Simulation
[7:30] Learning and Planning in Complex Action Spaces
[7:35] Model-Based Reinforcement Learning via Latent-Space Collocation
[7:40] Vector Quantized Models for Planning
[7:45] LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
8 a.m.
Affinity Workshop:
Queer in AI Workshop
(ends 5:00 PM)
Invited Talk:
Rethinking Drug Discovery in the Era of Digital Biology
Daphne Koller
(ends 9:00 AM)
9 a.m.
Poster Session 1 [9:00-11:00]
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
Winograd Algorithm for AdderNet
Fast Projection Onto Convex Smooth Constraints
Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Directional Graph Networks
Learning Bounds for Open-Set Learning
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Learning Intra-Batch Connections for Deep Metric Learning
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Interpretable Stability Bounds for Spectral Graph Filters
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Consistent Nonparametric Methods for Network Assisted Covariate Estimation
Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction
Targeted Data Acquisition for Evolving Negotiation Agents
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
Momentum Residual Neural Networks
Learning Task Informed Abstractions
A Functional Perspective on Learning Symmetric Functions with Neural Networks
Generative Adversarial Transformers
Bayesian Deep Learning via Subnetwork Inference
Thinking Like Transformers
Efficient Differentiable Simulation of Articulated Bodies
UnICORNN: A recurrent model for learning very long time dependencies
Self-Tuning for Data-Efficient Deep Learning
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks
Interactive Learning from Activity Description
Decoupling Value and Policy for Generalization in Reinforcement Learning
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
On the Random Conjugate Kernel and Neural Tangent Kernel
PID Accelerated Value Iteration Algorithm
Grid-Functioned Neural Networks
Dueling Convex Optimization
Multiplicative Noise and Heavy Tails in Stochastic Optimization
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
Not All Memories are Created Equal: Learning to Forget by Expiring
Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond
Inverse Constrained Reinforcement Learning
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Hierarchical VAEs Know What They Don’t Know
Graph Mixture Density Networks
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Unsupervised Learning of Visual 3D Keypoints for Control
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation
Reinforcement Learning with Prototypical Representations
Zoo-Tuning: Adaptive Transfer from A Zoo of Models
Breaking the Limits of Message Passing Graph Neural Networks
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
PixelTransformer: Sample Conditioned Signal Generation
Muesli: Combining Improvements in Policy Optimization
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
Provably Strict Generalisation Benefit for Equivariant Models
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Size-Invariant Graph Representations for Graph Classification Extrapolations
State Entropy Maximization with Random Encoders for Efficient Exploration
Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition
Sparsifying Networks via Subdifferential Inclusion
Provably Efficient Learning of Transferable Rewards
Connecting Sphere Manifolds Hierarchically for Regularization
Skill Discovery for Exploration and Planning using Deep Skill Graphs
What Are Bayesian Neural Network Posteriors Really Like?
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Unifying Vision-and-Language Tasks via Text Generation
Generative Video Transformer: Can Objects be the Words?
World Model as a Graph: Learning Latent Landmarks for Planning
Generative Particle Variational Inference via Estimation of Functional Gradients
Learning Curves for Analysis of Deep Networks
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Fundamental Tradeoffs in Distributionally Adversarial Training
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Self Normalizing Flows
Parameter-free Locally Accelerated Conditional Gradients
Projection Robust Wasserstein Barycenters
Outlier-Robust Optimal Transport
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Is Space-Time Attention All You Need for Video Understanding?
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
Vector Quantized Models for Planning
Principled Simplicial Neural Networks for Trajectory Prediction
Optimal Complexity in Decentralized Training
OmniNet: Omnidirectional Representations from Transformers
Better Training using Weight-Constrained Stochastic Dynamics
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Attention is not all you need: pure attention loses rank doubly exponentially with depth
Deeply-Debiased Off-Policy Interval Estimation
"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms
Preferential Temporal Difference Learning
Model-Based Reinforcement Learning via Latent-Space Collocation
Explainable Automated Graph Representation Learning with Hyperparameter Importance
Evolving Attention with Residual Convolutions
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Riemannian Convex Potential Maps
Acceleration via Fractal Learning Rate Schedules
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Offline Contextual Bandits with Overparameterized Models
Decomposable Submodular Function Minimization via Maximum Flow
Efficient Lottery Ticket Finding: Less Data is More
A Unified Lottery Ticket Hypothesis for Graph Neural Networks
MC-LSTM: Mass-Conserving LSTM
Improved Denoising Diffusion Probabilistic Models
On the Optimality of Batch Policy Optimization Algorithms
Making transport more robust and interpretable by moving data through a small number of anchor points
Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams
Newton Method over Networks is Fast up to the Statistical Precision
From Local Structures to Size Generalization in Graph Neural Networks
Low-Rank Sinkhorn Factorization
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
MSA Transformer
Federated Continual Learning with Weighted Inter-client Transfer
ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
How Framelets Enhance Graph Neural Networks
Distributed Second Order Methods with Fast Rates and Compressed Communication
BORE: Bayesian Optimization by Density-Ratio Estimation
Reinforcement Learning for Cost-Aware Markov Decision Processes
LieTransformer: Equivariant Self-Attention for Lie Groups
Bias-Robust Bayesian Optimization via Dueling Bandits
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
Variational Data Assimilation with a Learned Inverse Observation Operator
A Novel Sequential Coreset Method for Gradient Descent Algorithms
Dataset Dynamics via Gradient Flows in Probability Space
Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
PODS: Policy Optimization via Differentiable Simulation
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Neural Symbolic Regression that scales
Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Autoencoder Image Interpolation by Shaping the Latent Space
Strategic Classification Made Practical
A Probabilistic Approach to Neural Network Pruning
On Monotonic Linear Interpolation of Neural Network Parameters
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
Distributionally Robust Optimization with Markovian Data
Sliced Iterative Normalizing Flows
Federated Learning of User Verification Models Without Sharing Embeddings
Large-Margin Contrastive Learning with Distance Polarization Regularizer
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
AutoSampling: Search for Effective Data Sampling Schedules
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Parallelizing Legendre Memory Unit Training
A New Formalism, Method and Open Issues for Zero-Shot Coordination
Value Alignment Verification
Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
Federated Learning under Arbitrary Communication Patterns
E(n) Equivariant Graph Neural Networks
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface
KNAS: Green Neural Architecture Search
Neuro-algorithmic Policies Enable Fast Combinatorial Generalization
Deep kernel processes
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Neural Architecture Search without Training
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Perceiver: General Perception with Iterative Attention
Principal Component Hierarchy for Sparse Quadratic Programs
Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model
Towards Understanding Learning in Neural Networks with Linear Teachers
Exploiting structured data for learning contagious diseases under incomplete testing
Learning and Planning in Complex Action Spaces
Stochastic Sign Descent Methods: New Algorithms and Better Theory
Imitation by Predicting Observations
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Offline Reinforcement Learning with Pseudometric Learning
Relative Positional Encoding for Transformers with Linear Complexity
Autoencoding Under Normalization Constraints
DeepWalking Backwards: From Embeddings Back to Graphs
Learning Routines for Effective Off-Policy Reinforcement Learning
Leveraging Sparse Linear Layers for Debuggable Deep Networks
A New Representation of Successor Features for Transfer across Dissimilar Environments
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
One-sided Frank-Wolfe algorithms for saddle problems
Synthesizer: Rethinking Self-Attention for Transformer Models
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
Phasic Policy Gradient
(ends 11:00 AM)
11 a.m.
Town Hall:
Town Hall
(ends 12:00 PM)
5 p.m.
Deep Learning Algorithms 5 [5:00-6:00]
Oral s 5:00-5:20
[5:00] A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Spotlight s 5:20-5:50
[5:20] Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
[5:25] The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
[5:30] Signatured Deep Fictitious Play for Mean Field Games with Common Noise
[5:35] Equivariant message passing for the prediction of tensorial properties and molecular spectra
[5:40] Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.
[5:45] LARNet: Lie Algebra Residual Network for Face Recognition
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Deep Learning 2 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Spotlight s 5:20-5:50
[5:20] Stabilizing Equilibrium Models by Jacobian Regularization
[5:25] On the Predictability of Pruning Across Scales
[5:30] Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
[5:35] LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
[5:40] Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset
[5:45] Learning Neural Network Subspaces
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Deep Learning Algorithms 6 [5:00-6:00]
Oral s 5:00-5:20
[5:00] CATE: Computation-aware Neural Architecture Encoding with Transformers
Spotlight s 5:20-5:50
[5:20] What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
[5:25] Towards Domain-Agnostic Contrastive Learning
[5:30] Joining datasets via data augmentation in the label space for neural networks
[5:35] Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
[5:40] Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
[5:45] Poolingformer: Long Document Modeling with Pooling Attention
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Deep Generative Model 3 [5:00-6:00]
Oral s 5:00-5:20
[5:00] NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Spotlight s 5:20-5:50
[5:20] Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
[5:25] Soft then Hard: Rethinking the Quantization in Neural Image Compression
[5:30] Improved Contrastive Divergence Training of Energy-Based Models
[5:35] Deep Generative Learning via Schrödinger Bridge
[5:40] Partially Observed Exchangeable Modeling
[5:45] Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Reinforcement Learning 3 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Spotlight s 5:20-5:50
[5:20] Re-understanding Finite-State Representations of Recurrent Policy Networks
[5:25] Emergent Social Learning via Multi-agent Reinforcement Learning
[5:30] From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
[5:35] Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
[5:40] Trajectory Diversity for Zero-Shot Coordination
[5:45] FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Optimization 3 [5:00-6:00]
Oral s 5:00-5:20
[5:00] On the price of explainability for some clustering problems
Spotlight s 5:20-5:50
[5:20] Instance Specific Approximations for Submodular Maximization
[5:25] Adapting to Delays and Data in Adversarial Multi-Armed Bandits
[5:30] Structured Convolutional Kernel Networks for Airline Crew Scheduling
[5:35] Online Graph Dictionary Learning
[5:40] Stochastic Iterative Graph Matching
[5:45] Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Optimization and Algorithms 1 [5:00-6:00]
Oral s 5:00-5:20
[5:00] A Tale of Two Efficient and Informative Negative Sampling Distributions
Spotlight s 5:20-5:50
[5:20] TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
[5:25] Quantization Algorithms for Random Fourier Features
[5:30] Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
[5:35] Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability
[5:40] Heterogeneity for the Win: One-Shot Federated Clustering
[5:45] Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Reinforcement Learning and Planning 3 [5:00-6:00]
Oral s 5:00-5:20
[5:00] PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Spotlight s 5:20-5:50
[5:20] Safe Reinforcement Learning with Linear Function Approximation
[5:25] Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
[5:30] Offline Reinforcement Learning with Fisher Divergence Critic Regularization
[5:35] Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
[5:40] OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
[5:45] Discovering symbolic policies with deep reinforcement learning
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Reinforcement Learning 4 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Robust Asymmetric Learning in POMDPs
Spotlight s 5:20-5:50
[5:20] Differentiable Spatial Planning using Transformers
[5:25] Convex Regularization in Monte-Carlo Tree Search
[5:30] On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
[5:35] Multi-Task Reinforcement Learning with Context-based Representations
[5:40] High Confidence Generalization for Reinforcement Learning
[5:45] Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
6 p.m.
AutoML [6:00-7:00]
Spotlight s 6:00-6:15
[6:00] iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
[6:05] Accurate Post Training Quantization With Small Calibration Sets
[6:10] Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Oral s 6:15-6:35
[6:15] Few-Shot Neural Architecture Search
Spotlight s 6:35-6:50
[6:35] AutoAttend: Automated Attention Representation Search
[6:40] Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
[6:45] Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
Deep Learning Algorithms and Applications [6:00-7:00]
Oral s 6:00-6:20
[6:00] Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
Spotlight s 6:20-6:45
[6:20] EfficientNetV2: Smaller Models and Faster Training
[6:25] Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
[6:30] LAMDA: Label Matching Deep Domain Adaptation
[6:35] Temporally Correlated Task Scheduling for Sequence Learning
[6:40] Information Obfuscation of Graph Neural Networks
Q&A s 6:45-6:50
[6:45] Q&A
(ends 7:00 PM)
Deep Generative Model 4 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Generating images with sparse representations
Spotlight s 6:20-6:50
[6:20] An Identifiable Double VAE For Disentangled Representations
[6:25] A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
[6:30] On Characterizing GAN Convergence Through Proximal Duality Gap
[6:35] Scalable Normalizing Flows for Permutation Invariant Densities
[6:40] Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
[6:45] Zero-Shot Text-to-Image Generation
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
Reinforcement Learning 5 [6:00-7:00]
Oral s 6:00-6:20
[6:00] The Emergence of Individuality
Spotlight s 6:20-6:45
[6:20] DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
[6:25] From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments
[6:30] Learning While Playing in Mean-Field Games: Convergence and Optimality
[6:35] Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
[6:40] Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Q&A s 6:45-6:50
[6:45] Q&A
(ends 7:00 PM)
Optimization and Algorithms 2 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Network Inference and Influence Maximization from Samples
Spotlight s 6:20-6:50
[6:20] Regularized Submodular Maximization at Scale
[6:25] Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data
[6:30] Connecting Interpretability and Robustness in Decision Trees through Separation
[6:35] Light RUMs
[6:40] Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
[6:45] CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
Optimization (Stochastic) [6:00-7:00]
Oral s 6:00-6:20
[6:00] The Power of Adaptivity for Stochastic Submodular Cover
Spotlight s 6:20-6:50
[6:20] The Heavy-Tail Phenomenon in SGD
[6:25] Federated Composite Optimization
[6:30] On Estimation in Latent Variable Models
[6:35] Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge
[6:40] Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint
[6:45] BASGD: Buffered Asynchronous SGD for Byzantine Learning
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
Algorithms 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] A Wasserstein Minimax Framework for Mixed Linear Regression
Spotlight s 6:20-6:50
[6:20] Weight-covariance alignment for adversarially robust neural networks
[6:25] Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
[6:30] Communication-Efficient Distributed SVD via Local Power Iterations
[6:35] A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
[6:40] Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
[6:45] Leveraging Language to Learn Program Abstractions and Search Heuristics
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
Reinforcement Learning 6 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Decoupling Value and Policy for Generalization in Reinforcement Learning
Spotlight s 6:20-6:50
[6:20] Prioritized Level Replay
[6:25] SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
[6:30] GMAC: A Distributional Perspective on Actor-Critic Framework
[6:35] Goal-Conditioned Reinforcement Learning with Imagined Subgoals
[6:40] Policy Gradient Bayesian Robust Optimization for Imitation Learning
[6:45] Reinforcement Learning of Implicit and Explicit Control Flow Instructions
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
Reinforcement Learning 7 [6:00-7:00]
Oral s 6:00-6:20
[6:00] PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
Spotlight s 6:20-6:50
[6:20] Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
[6:25] Keyframe-Focused Visual Imitation Learning
[6:30] Learning and Planning in Average-Reward Markov Decision Processes
[6:35] Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
[6:40] Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
[6:45] Emphatic Algorithms for Deep Reinforcement Learning
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 PM)
7 p.m.
AutoML and Neural Network Architectures 2 [7:00-8:00]
Oral s 7:00-7:20
[7:00] AlphaNet: Improved Training of Supernets with Alpha-Divergence
Spotlight s 7:20-7:50
[7:20] Catformer: Designing Stable Transformers via Sensitivity Analysis
[7:25] A Receptor Skeleton for Capsule Neural Networks
[7:30] Explore Visual Concept Formation for Image Classification
[7:35] K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
[7:40] High-Performance Large-Scale Image Recognition Without Normalization
[7:45] Lipschitz normalization for self-attention layers with application to graph neural networks
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Deep Learning Algorithms 7 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Out-of-Distribution Generalization via Risk Extrapolation (REx)
Spotlight s 7:20-7:50
[7:20] What Makes for End-to-End Object Detection?
[7:25] On Explainability of Graph Neural Networks via Subgraph Explorations
[7:30] Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
[7:35] Data Augmentation for Meta-Learning
[7:40] Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
[7:45] Neural Symbolic Regression that scales
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Deep Learning Algorithms 8 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Just Train Twice: Improving Group Robustness without Training Group Information
Spotlight s 7:20-7:50
[7:20] Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
[7:25] GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
[7:30] A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
[7:35] Neural Rough Differential Equations for Long Time Series
[7:40] Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
[7:45] Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Optimization 4 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Spotlight s 7:20-7:50
[7:20] Oblivious Sketching-based Central Path Method for Linear Programming
[7:25] Bayesian Optimization over Hybrid Spaces
[7:30] Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
[7:35] Compositional Video Synthesis with Action Graphs
[7:40] Neural Pharmacodynamic State Space Modeling
[7:45] Three Operator Splitting with a Nonconvex Loss Function
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Optimization (Convex) 2 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm
Spotlight s 7:20-7:50
[7:20] Communication-Efficient Distributed Optimization with Quantized Preconditioners
[7:25] Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
[7:30] Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
[7:35] Moreau-Yosida $f$-divergences
[7:40] Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
[7:45] On a Combination of Alternating Minimization and Nesterov's Momentum
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Large Scale Optimization [7:00-8:00]
Oral s 7:00-7:20
[7:00] ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
Spotlight s 7:20-7:50
[7:20] Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers
[7:25] Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks
[7:30] Training Graph Neural Networks with 1000 Layers
[7:35] 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed
[7:40] Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity
[7:45] Ditto: Fair and Robust Federated Learning Through Personalization
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Reinforcement Learning 8 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Spotlight s 7:20-7:50
[7:20] On Proximal Policy Optimization's Heavy-tailed Gradients
[7:25] Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
[7:30] Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
[7:35] Is Pessimism Provably Efficient for Offline RL?
[7:40] Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
[7:45] Density Constrained Reinforcement Learning
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Deep Reinforcement Learning 3 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Spotlight s 7:20-7:50
[7:20] A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
[7:25] Learning to Weight Imperfect Demonstrations
[7:30] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
[7:35] MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
[7:40] RRL: Resnet as representation for Reinforcement Learning
[7:45] SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
Reinforcement Learning 9 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Hyperparameter Selection for Imitation Learning
Spotlight s 7:20-7:50
[7:20] Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning
[7:25] Monotonic Robust Policy Optimization with Model Discrepancy
[7:30] Taylor Expansion of Discount Factors
[7:35] Generalizable Episodic Memory for Deep Reinforcement Learning
[7:40] Representation Matters: Offline Pretraining for Sequential Decision Making
[7:45] Reinforcement Learning Under Moral Uncertainty
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 PM)
8 p.m.
Invited Talk:
Cryospheric Science and Emergence of Machine Learning
Xiao Cunde · Qin Dahe
(ends 9:00 PM)
9 p.m.
Poster Session 2 [9:00-11:00]
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
AlphaNet: Improved Training of Supernets with Alpha-Divergence
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers
CATE: Computation-aware Neural Architecture Encoding with Transformers
Taylor Expansion of Discount Factors
On the price of explainability for some clustering problems
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
BASGD: Buffered Asynchronous SGD for Byzantine Learning
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Federated Composite Optimization
LARNet: Lie Algebra Residual Network for Face Recognition
Representation Matters: Offline Pretraining for Sequential Decision Making
Partially Observed Exchangeable Modeling
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
On Characterizing GAN Convergence Through Proximal Duality Gap
GMAC: A Distributional Perspective on Actor-Critic Framework
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint
Safe Reinforcement Learning with Linear Function Approximation
Is Pessimism Provably Efficient for Offline RL?
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
Generalizable Episodic Memory for Deep Reinforcement Learning
Re-understanding Finite-State Representations of Recurrent Policy Networks
A Wasserstein Minimax Framework for Mixed Linear Regression
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
Towards Domain-Agnostic Contrastive Learning
Moreau-Yosida $f$-divergences
Training Graph Neural Networks with 1000 Layers
Generating images with sparse representations
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Structured Convolutional Kernel Networks for Airline Crew Scheduling
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Data Augmentation for Meta-Learning
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Just Train Twice: Improving Group Robustness without Training Group Information
Learning and Planning in Average-Reward Markov Decision Processes
Instance Specific Approximations for Submodular Maximization
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning
Differentiable Spatial Planning using Transformers
Reinforcement Learning Under Moral Uncertainty
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Poolingformer: Long Document Modeling with Pooling Attention
Lipschitz normalization for self-attention layers with application to graph neural networks
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Regularized Submodular Maximization at Scale
Prioritized Level Replay
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
On a Combination of Alternating Minimization and Nesterov's Momentum
Ditto: Fair and Robust Federated Learning Through Personalization
Leveraging Language to Learn Program Abstractions and Search Heuristics
Three Operator Splitting with a Nonconvex Loss Function
Hyperparameter Selection for Imitation Learning
Temporally Correlated Task Scheduling for Sequence Learning
A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
A Tale of Two Efficient and Informative Negative Sampling Distributions
AutoAttend: Automated Attention Representation Search
Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
On the Predictability of Pruning Across Scales
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
On Estimation in Latent Variable Models
Multi-Task Reinforcement Learning with Context-based Representations
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
What Makes for End-to-End Object Detection?
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks
Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge
Deep Generative Learning via Schrödinger Bridge
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
On Proximal Policy Optimization's Heavy-tailed Gradients
Learning While Playing in Mean-Field Games: Convergence and Optimality
Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability
Accurate Post Training Quantization With Small Calibration Sets
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Emphatic Algorithms for Deep Reinforcement Learning
Connecting Interpretability and Robustness in Decision Trees through Separation
Stabilizing Equilibrium Models by Jacobian Regularization
Network Inference and Influence Maximization from Samples
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset
Goal-Conditioned Reinforcement Learning with Imagined Subgoals
Discovering symbolic policies with deep reinforcement learning
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
Few-Shot Neural Architecture Search
High Confidence Generalization for Reinforcement Learning
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
Signatured Deep Fictitious Play for Mean Field Games with Common Noise
Scalable Normalizing Flows for Permutation Invariant Densities
Zero-Shot Text-to-Image Generation
Explore Visual Concept Formation for Image Classification
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data
Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data
Trajectory Diversity for Zero-Shot Coordination
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
Bayesian Optimization over Hybrid Spaces
Quantization Algorithms for Random Fourier Features
The Emergence of Individuality
Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm
Light RUMs
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
Improved Contrastive Divergence Training of Energy-Based Models
Keyframe-Focused Visual Imitation Learning
Reinforcement Learning of Implicit and Explicit Control Flow Instructions
High-Performance Large-Scale Image Recognition Without Normalization
Learning to Weight Imperfect Demonstrations
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Monotonic Robust Policy Optimization with Model Discrepancy
Convex Regularization in Monte-Carlo Tree Search
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Stochastic Iterative Graph Matching
Weight-covariance alignment for adversarially robust neural networks
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Online Graph Dictionary Learning
On Explainability of Graph Neural Networks via Subgraph Explorations
An Identifiable Double VAE For Disentangled Representations
Emergent Social Learning via Multi-agent Reinforcement Learning
Information Obfuscation of Graph Neural Networks
The Heavy-Tail Phenomenon in SGD
Policy Gradient Bayesian Robust Optimization for Imitation Learning
LAMDA: Label Matching Deep Domain Adaptation
Compositional Video Synthesis with Action Graphs
The Power of Adaptivity for Stochastic Submodular Cover
Robust Asymmetric Learning in POMDPs
Density Constrained Reinforcement Learning
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
A Receptor Skeleton for Capsule Neural Networks
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
Communication-Efficient Distributed SVD via Local Power Iterations
Catformer: Designing Stable Transformers via Sensitivity Analysis
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Communication-Efficient Distributed Optimization with Quantized Preconditioners
Heterogeneity for the Win: One-Shot Federated Clustering
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
RRL: Resnet as representation for Reinforcement Learning
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Learning Neural Network Subspaces
Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning
EfficientNetV2: Smaller Models and Faster Training
Joining datasets via data augmentation in the label space for neural networks
Oblivious Sketching-based Central Path Method for Linear Programming
Neural Rough Differential Equations for Long Time Series
(ends 11:00 PM)
WED 21 JUL
5 a.m.
Deep Learning Theory 2 [5:00-6:00]
Oral s 5:00-5:20
[5:00] On Energy-Based Models with Overparametrized Shallow Neural Networks
Spotlight s 5:20-5:50
[5:20] Uncertainty Principles of Encoding GANs
[5:25] On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
[5:30] Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
[5:35] Functional Space Analysis of Local GAN Convergence
[5:40] Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
[5:45] Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Optimization (Nonconvex) [5:00-6:00]
Oral s 5:00-5:20
[5:00] Optimizing persistent homology based functions
Spotlight s 5:20-5:50
[5:20] Debiasing a First-order Heuristic for Approximate Bi-level Optimization
[5:25] SMG: A Shuffling Gradient-Based Method with Momentum
[5:30] Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
[5:35] MARINA: Faster Non-Convex Distributed Learning with Compression
[5:40] Bilevel Optimization: Convergence Analysis and Enhanced Design
[5:45] Learning from History for Byzantine Robust Optimization
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Probabilistic Methods 1 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Spotlight s 5:20-5:50
[5:20] Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
[5:25] Kernel Continual Learning
[5:30] XOR-CD: Linearly Convergent Constrained Structure Generation
[5:35] ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
[5:40] Composing Normalizing Flows for Inverse Problems
[5:45] Nonparametric Hamiltonian Monte Carlo
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Reinforcement Learning 10 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Cross-domain Imitation from Observations
Spotlight s 5:20-5:50
[5:20] SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
[5:25] Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
[5:30] Active Feature Acquisition with Generative Surrogate Models
[5:35] Characterizing the Gap Between Actor-Critic and Policy Gradient
[5:40] Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
[5:45] Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Reinforcement Learning 11 [5:00-6:00]
Oral s 5:00-5:20
[5:00] APS: Active Pretraining with Successor Features
Spotlight s 5:20-5:50
[5:20] Guided Exploration with Proximal Policy Optimization using a Single Demonstration
[5:25] Self-Paced Context Evaluation for Contextual Reinforcement Learning
[5:30] Unsupervised Skill Discovery with Bottleneck Option Learning
[5:35] TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
[5:40] Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
[5:45] Data-efficient Hindsight Off-policy Option Learning
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Learning Theory 1 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Near Optimal Reward-Free Reinforcement Learning
Spotlight s 5:20-5:50
[5:20] Batch Value-function Approximation with Only Realizability
[5:25] Adversarial Combinatorial Bandits with General Non-linear Reward Functions
[5:30] Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
[5:35] Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
[5:40] On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
[5:45] Spectral vertex sparsifiers and pair-wise spanners over distributed graphs
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Supervised Learning 1 [5:00-6:00]
Oral s 5:00-5:20
[5:00] When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC
Spotlight s 5:20-5:50
[5:20] SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels
[5:25] Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces
[5:30] Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization
[5:35] Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
[5:40] Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
[5:45] Meta-learning Hyperparameter Performance Prediction with Neural Processes
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Learning Theory 2 [5:00-6:00]
Oral s 5:00-5:20
[5:00] Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
Spotlight s 5:20-5:50
[5:20] Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
[5:25] Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
[5:30] Robust Inference for High-Dimensional Linear Models via Residual Randomization
[5:35] Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
[5:40] Generalization Guarantees for Neural Architecture Search with Train-Validation Split
[5:45] Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
Learning Theory 3 [5:00-6:00]
Oral s 5:00-5:20
[5:00] The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets
Spotlight s 5:20-5:50
[5:20] Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
[5:25] Approximating a Distribution Using Weight Queries
[5:30] Estimating $\alpha$-Rank from A Few Entries with Low Rank Matrix Completion
[5:35] Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
[5:40] Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
[5:45] Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 AM)
6 a.m.
Algorithms and Applications [6:00-7:00]
Oral s 6:00-6:20
[6:00] Elastic Graph Neural Networks
Spotlight s 6:20-6:50
[6:20] How could Neural Networks understand Programs?
[6:25] ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
[6:30] How Do Adam and Training Strategies Help BNNs Optimization
[6:35] Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
[6:40] Learning from Nested Data with Ornstein Auto-Encoders
[6:45] Kernel-Based Reinforcement Learning: A Finite-Time Analysis
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Online Learning 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
Spotlight s 6:20-6:50
[6:20] Near-Optimal Confidence Sequences for Bounded Random Variables
[6:25] Joint Online Learning and Decision-making via Dual Mirror Descent
[6:30] Online A-Optimal Design and Active Linear Regression
[6:35] Fairness and Bias in Online Selection
[6:40] ChaCha for Online AutoML
[6:45] An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Reinforcement Learning 12 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
Spotlight s 6:20-6:50
[6:20] Principled Exploration via Optimistic Bootstrapping and Backward Induction
[6:25] Ensemble Bootstrapping for Q-Learning
[6:30] Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm
[6:35] A Regret Minimization Approach to Iterative Learning Control
[6:40] TempoRL: Learning When to Act
[6:45] State Relevance for Off-Policy Evaluation
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Reinforcement Learning and Optimization [6:00-7:00]
Oral s 6:00-6:20
[6:00] Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Spotlight s 6:20-6:50
[6:20] Megaverse: Simulating Embodied Agents at One Million Experiences per Second
[6:25] Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
[6:30] Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
[6:35] Off-Belief Learning
[6:40] On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
[6:45] Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Optimization and Algorithms 3 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Dynamic Game Theoretic Neural Optimizer
Spotlight s 6:20-6:50
[6:20] Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
[6:25] A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
[6:30] Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
[6:35] Tractable structured natural-gradient descent using local parameterizations
[6:40] Towards Rigorous Interpretations: a Formalisation of Feature Attribution
[6:45] Distributed Nystr\"{o}m Kernel Learning with Communications
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Reinforcement Learning Theory 1 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Bilinear Classes: A Structural Framework for Provable Generalization in RL
Spotlight s 6:20-6:50
[6:20] Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
[6:25] Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
[6:30] Reward Identification in Inverse Reinforcement Learning
[6:35] Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
[6:40] Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
[6:45] Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Supervised Learning 2 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Understanding Instance-Level Label Noise: Disparate Impacts and Treatments
Spotlight s 6:20-6:50
[6:20] Selecting Data Augmentation for Simulating Interventions
[6:25] Training Data Subset Selection for Regression with Controlled Generalization Error
[6:30] Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
[6:35] Learning from Noisy Labels with No Change to the Training Process
[6:40] What does LIME really see in images?
[6:45] Narrow Margins: Classification, Margins and Fat Tails
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Learning Theory 4 [6:00-7:00]
Oral s 6:00-6:20
[6:00] Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Spotlight s 6:20-6:50
[6:20] Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
[6:25] A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
[6:30] Estimation and Quantization of Expected Persistence Diagrams
[6:35] Post-selection inference with HSIC-Lasso
[6:40] Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
[6:45] Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
Game Theory and Econ [6:00-7:00]
Oral s 6:00-6:20
[6:00] Reserve Price Optimization for First Price Auctions in Display Advertising
Spotlight s 6:20-6:50
[6:20] Align, then memorise: the dynamics of learning with feedback alignment
[6:25] Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results
[6:30] Learning to Price Against a Moving Target
[6:35] Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
[6:40] Approximate Group Fairness for Clustering
[6:45] Incentivizing Compliance with Algorithmic Instruments
Q&A s 6:50-6:55
[6:50] Q&A
(ends 7:00 AM)
6:40 a.m.
Affinity Workshop:
Women in Machine Learning (WiML) Un-Workshop
(ends 8:45 PM)
7 a.m.
Bandits 1 [7:00-8:00]
Oral s 7:00-7:20
[7:00] High-dimensional Experimental Design and Kernel Bandits
Spotlight s 7:20-7:50
[7:20] Dichotomous Optimistic Search to Quantify Human Perception
[7:25] Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits
[7:30] Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
[7:35] Deciding What to Learn: A Rate-Distortion Approach
[7:40] No-regret Algorithms for Capturing Events in Poisson Point Processes
[7:45] Parametric Graph for Unimodal Ranking Bandit
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Reinforcement Learning and Bandits [7:00-8:00]
Spotlight s 7:00-7:45
[7:00] Instabilities of Offline RL with Pre-Trained Neural Representation
[7:05] Path Planning using Neural A* Search
[7:10] Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
[7:15] Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
[7:20] Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
[7:25] Continuous-time Model-based Reinforcement Learning
[7:30] Bayesian Optimistic Optimisation with Exponentially Decaying Regret
[7:35] Best Model Identification: A Rested Bandit Formulation
[7:40] Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
Q&A s 7:45-7:50
[7:45] Q&A
(ends 8:00 AM)
Applications (Bio) 1 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations
Spotlight s 7:20-7:50
[7:20] Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
[7:25] Deep Continuous Networks
[7:30] SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
[7:35] Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction
[7:40] On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
[7:45] AGENT: A Benchmark for Core Psychological Reasoning
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Deep Learning Theory 3 [7:00-8:00]
Oral s 7:00-7:20
[7:00] On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Spotlight s 7:20-7:50
[7:20] A statistical perspective on distillation
[7:25] The Lipschitz Constant of Self-Attention
[7:30] Revealing the Structure of Deep Neural Networks via Convex Duality
[7:35] Representational aspects of depth and conditioning in normalizing flows
[7:40] Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
[7:45] The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Reinforcement Learning 13 [7:00-8:00]
Oral s 7:00-7:40
[7:00] The Logical Options Framework
[7:20] On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Spotlight s 7:40-7:50
[7:40] Adversarial Option-Aware Hierarchical Imitation Learning
[7:45] Value Iteration in Continuous Actions, States and Time
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Time Series 1 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Inferring serial correlation with dynamic backgrounds
Spotlight s 7:20-7:50
[7:20] Variance Reduced Training with Stratified Sampling for Forecasting Models
[7:25] Necessary and sufficient conditions for causal feature selection in time series with latent common causes
[7:30] Multiplying Matrices Without Multiplying
[7:35] The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
[7:40] Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
[7:45] Learning Stochastic Behaviour from Aggregate Data
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Learning Theory 5 [7:00-8:00]
Oral s 7:00-7:20
[7:00] Modelling Behavioural Diversity for Learning in Open-Ended Games
Spotlight s 7:20-7:50
[7:20] Follow-the-Regularized-Leader Routes to Chaos in Routing Games
[7:25] How to Learn when Data Reacts to Your Model: Performative Gradient Descent
[7:30] Continuous Coordination As a Realistic Scenario for Lifelong Learning
[7:35] Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
[7:40] Collaborative Bayesian Optimization with Fair Regret
[7:45] Exponentially Many Local Minima in Quantum Neural Networks
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Kernel Methods [7:00-8:00]
Oral s 7:00-7:20
[7:00] Kernel Stein Discrepancy Descent
Spotlight s 7:20-7:50
[7:20] Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
[7:25] Generalised Lipschitz Regularisation Equals Distributional Robustness
[7:30] Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold
[7:35] An exact solver for the Weston-Watkins SVM subproblem
[7:40] Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
[7:45] Faster Kernel Matrix Algebra via Density Estimation
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Sparsity and Compressed Sensing [7:00-8:00]
Oral s 7:00-7:20
[7:00] Measuring Robustness in Deep Learning Based Compressive Sensing
Spotlight s 7:20-7:50
[7:20] Instance-Optimal Compressed Sensing via Posterior Sampling
[7:25] A Nullspace Property for Subspace-Preserving Recovery
[7:30] Homomorphic Sensing: Sparsity and Noise
[7:35] Active Deep Probabilistic Subsampling
[7:40] Prior Image-Constrained Reconstruction using Style-Based Generative Models
[7:45] Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
Learning Theory 6 [7:00-8:00]
Oral s 7:00-7:20
[7:00] PAC-Learning for Strategic Classification
Spotlight s 7:20-7:50
[7:20] Learning from Biased Data: A Semi-Parametric Approach
[7:25] Learning in Nonzero-Sum Stochastic Games with Potentials
[7:30] Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
[7:35] Large-Scale Multi-Agent Deep FBSDEs
[7:40] Multi-group Agnostic PAC Learnability
[7:45] One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
Q&A s 7:50-7:55
[7:50] Q&A
(ends 8:00 AM)
8 a.m.
Invited Talk:
Esther Duflo, Plumbers and Mechanics: How ML can complement RCT in policy experiments
Esther Duflo
(ends 9:00 AM)
9 a.m.
Poster Session 3 [9:00-11:00]
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Estimating $\alpha$-Rank from A Few Entries with Low Rank Matrix Completion
MARINA: Faster Non-Convex Distributed Learning with Compression
Tractable structured natural-gradient descent using local parameterizations
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
The Logical Options Framework
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Joint Online Learning and Decision-making via Dual Mirror Descent
Path Planning using Neural A* Search
TempoRL: Learning When to Act
A statistical perspective on distillation
Bilinear Classes: A Structural Framework for Provable Generalization in RL
Self-Paced Context Evaluation for Contextual Reinforcement Learning
How Do Adam and Training Strategies Help BNNs Optimization
Near Optimal Reward-Free Reinforcement Learning
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
Dynamic Game Theoretic Neural Optimizer
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
An exact solver for the Weston-Watkins SVM subproblem
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits
Kernel Stein Discrepancy Descent
AGENT: A Benchmark for Core Psychological Reasoning
Active Feature Acquisition with Generative Surrogate Models
Generalised Lipschitz Regularisation Equals Distributional Robustness
Approximating a Distribution Using Weight Queries
Learning from History for Byzantine Robust Optimization
A Regret Minimization Approach to Iterative Learning Control
Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
Instance-Optimal Compressed Sensing via Posterior Sampling
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
Continuous Coordination As a Realistic Scenario for Lifelong Learning
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Batch Value-function Approximation with Only Realizability
Revealing the Structure of Deep Neural Networks via Convex Duality
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Ensemble Bootstrapping for Q-Learning
APS: Active Pretraining with Successor Features
Distributed Nystr\"{o}m Kernel Learning with Communications
Fairness and Bias in Online Selection
SMG: A Shuffling Gradient-Based Method with Momentum
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
Megaverse: Simulating Embodied Agents at One Million Experiences per Second
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold
On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
Deep Continuous Networks
Best Model Identification: A Rested Bandit Formulation
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
Elastic Graph Neural Networks
On Energy-Based Models with Overparametrized Shallow Neural Networks
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
Bilevel Optimization: Convergence Analysis and Enhanced Design
Selecting Data Augmentation for Simulating Interventions
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
Reward Identification in Inverse Reinforcement Learning
Post-selection inference with HSIC-Lasso
Principled Exploration via Optimistic Bootstrapping and Backward Induction
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
Incentivizing Compliance with Algorithmic Instruments
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
Robust Inference for High-Dimensional Linear Models via Residual Randomization
Collaborative Bayesian Optimization with Fair Regret
State Relevance for Off-Policy Evaluation
Necessary and sufficient conditions for causal feature selection in time series with latent common causes
Nonparametric Hamiltonian Monte Carlo
Multi-group Agnostic PAC Learnability
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Exponentially Many Local Minima in Quantum Neural Networks
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Debiasing a First-order Heuristic for Approximate Bi-level Optimization
Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
Align, then memorise: the dynamics of learning with feedback alignment
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Optimizing persistent homology based functions
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
Reserve Price Optimization for First Price Auctions in Display Advertising
What does LIME really see in images?
Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
PAC-Learning for Strategic Classification
Variance Reduced Training with Stratified Sampling for Forecasting Models
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Unsupervised Skill Discovery with Bottleneck Option Learning
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Representational aspects of depth and conditioning in normalizing flows
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Training Data Subset Selection for Regression with Controlled Generalization Error
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Near-Optimal Confidence Sequences for Bounded Random Variables
No-regret Algorithms for Capturing Events in Poisson Point Processes
Meta-learning Hyperparameter Performance Prediction with Neural Processes
Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Learning Stochastic Behaviour from Aggregate Data
Functional Space Analysis of Local GAN Convergence
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
Instabilities of Offline RL with Pre-Trained Neural Representation
A Nullspace Property for Subspace-Preserving Recovery
Understanding Instance-Level Label Noise: Disparate Impacts and Treatments
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
Active Deep Probabilistic Subsampling
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
High-dimensional Experimental Design and Kernel Bandits
Relative Deviation Margin Bounds
Deciding What to Learn: A Rate-Distortion Approach
The Lipschitz Constant of Self-Attention
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
Narrow Margins: Classification, Margins and Fat Tails
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces
ChaCha for Online AutoML
Online A-Optimal Design and Active Linear Regression
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs
XOR-CD: Linearly Convergent Constrained Structure Generation
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm
Value Iteration in Continuous Actions, States and Time
Characterizing the Gap Between Actor-Critic and Policy Gradient
Approximate Group Fairness for Clustering
Learning in Nonzero-Sum Stochastic Games with Potentials
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Estimation and Quantization of Expected Persistence Diagrams
Large-Scale Multi-Agent Deep FBSDEs
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Parametric Graph for Unimodal Ranking Bandit
Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed
SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels
Modelling Behavioural Diversity for Learning in Open-Ended Games
Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
Homomorphic Sensing: Sparsity and Noise
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
Dichotomous Optimistic Search to Quantify Human Perception
Learning from Biased Data: A Semi-Parametric Approach
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
Measuring Robustness in Deep Learning Based Compressive Sensing
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
Follow-the-Regularized-Leader Routes to Chaos in Routing Games
Faster Kernel Matrix Algebra via Density Estimation
Learning from Noisy Labels with No Change to the Training Process
Off-Belief Learning
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
How could Neural Networks understand Programs?
Learning from Nested Data with Ornstein Auto-Encoders
Inferring serial correlation with dynamic backgrounds
Uncertainty Principles of Encoding GANs
Composing Normalizing Flows for Inverse Problems
Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Cross-domain Imitation from Observations
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Adversarial Option-Aware Hierarchical Imitation Learning
Continuous-time Model-based Reinforcement Learning
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Multiplying Matrices Without Multiplying
Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Kernel Continual Learning
Data-efficient Hindsight Off-policy Option Learning
Learning to Price Against a Moving Target
(ends 11:00 AM)
1 p.m.
3:30 p.m.
5 p.m.
Bandits 2 [5:00-6:00]
Oral s 5:00-5:20
[5:00] The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
Spotlight s 5:20-5:50
[5:20] Dynamic Planning and Learning under Recovering Rewards
[5:25] Best Arm Identification in Graphical Bilinear Bandits
[5:30] Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
[5:35] Incentivized Bandit Learning with Self-Reinforcing User Preferences
[5:40] Approximation Theory Based Methods for RKHS Bandits
[5:45] Dynamic Balancing for Model Selection in Bandits and RL
Q&A s 5:50-5:55
[5:50] Q&A
(ends 6:00 PM)
Deep Learning Theory 4 [5:00-6:00]
Oral s 5:00-5:20
[5:00]