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SUN 18 JUL

5 a.m.

6 a.m.

7 a.m.

Expo Talk Panel:

(ends 8:00 AM)

8 a.m.

Expo Talk Panel:

(ends 9:00 AM)

9 a.m.

10 a.m.

5 p.m.

Expo Talk Panel:

(ends 5:59 PM)

7 p.m.

9 p.m.

MON 19 JUL

6 a.m.

8 a.m.

Tutorial:

(ends 11:15 AM)

Tutorial:

(ends 10:59 AM)

Tutorial:

(ends 11:00 AM)

noon

5 p.m.

6 p.m.

8 p.m.

TUE 20 JUL

5 a.m.

Orals 5:00-5:20

[5:00]
BORE: Bayesian Optimization by Density-Ratio Estimation

Spotlights 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&As 5:45-5:50

[5:45]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Relative Positional Encoding for Transformers with Linear Complexity

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Attention is not all you need: pure attention loses rank doubly exponentially with depth

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Size-Invariant Graph Representations for Graph Classification Extrapolations

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Spotlights 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&As 5:45-5:50

[5:45]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Optimal Complexity in Decentralized Training

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Phasic Policy Gradient

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Deeply-Debiased Off-Policy Interval Estimation

Spotlights 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&As 5:45-5:50

[5:45]
Q&A

(ends 6:00 AM)

6 a.m.

Orals 6:00-6:20

[6:00]
Neural Architecture Search without Training

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
Leveraging Sparse Linear Layers for Debuggable Deep Networks

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
What Are Bayesian Neural Network Posteriors Really Like?

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
Principled Simplicial Neural Networks for Trajectory Prediction

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

Spotlights 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 Donâ€™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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

Spotlights 6:20-6:50

[6:20]
Projection Robust Wasserstein Barycenters

[6:25]
Efficient Message Passing for 0â€“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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

Orals 6:00-6:20

[6:00]
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 AM)

7 a.m.

Orals 7:00-7:20

[7:00]
OmniNet: Omnidirectional Representations from Transformers

Spotlights 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&As 7:45-7:50

[7:45]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
Directional Graph Networks

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
Not All Memories are Created Equal: Learning to Forget by Expiring

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
World Model as a Graph: Learning Latent Landmarks for Planning

Spotlights 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&As 7:45-7:50

[7:45]
Q&A

(ends 8:00 AM)

Orals 7:00-7:20

[7:00]
Skill Discovery for Exploration and Planning using Deep Skill Graphs

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 AM)

8 a.m.

9 a.m.

Posters 9:00-11:00

(ends 11:00 AM)

11 a.m.

5 p.m.

Orals 5:00-5:20

[5:00]
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
Characterizing Structural Regularities of Labeled Data in Overparameterized Models

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
CATE: Computation-aware Neural Architecture Encoding with Transformers

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
NeRF-VAE: A Geometry Aware 3D Scene Generative Model

Spotlights 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 SchrÃ¶dinger Bridge

[5:40]
Partially Observed Exchangeable Modeling

[5:45]
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

Q&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

Spotlights 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 PoincarÃ© 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
On the price of explainability for some clustering problems

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
A Tale of Two Efficient and Informative Negative Sampling Distributions

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

Orals 5:00-5:20

[5:00]
Robust Asymmetric Learning in POMDPs

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 PM)

6 p.m.

Spotlights 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

Orals 6:15-6:35

[6:15]
Few-Shot Neural Architecture Search

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

Spotlights 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&As 6:45-6:50

[6:45]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
Generating images with sparse representations

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
The Emergence of Individuality

Spotlights 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&As 6:45-6:50

[6:45]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
Network Inference and Influence Maximization from Samples

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
The Power of Adaptivity for Stochastic Submodular Cover

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
A Wasserstein Minimax Framework for Mixed Linear Regression

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
Decoupling Value and Policy for Generalization in Reinforcement Learning

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

Orals 6:00-6:20

[6:00]
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Spotlights 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&As 6:50-6:55

[6:50]
Q&A

(ends 7:00 PM)

7 p.m.

Orals 7:00-7:20

[7:00]
AlphaNet: Improved Training of Supernets with Alpha-Divergence

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Out-of-Distribution Generalization via Risk Extrapolation (REx)

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Just Train Twice: Improving Group Robustness without Training Group Information

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Inverse Decision Modeling: Learning Interpretable Representations of Behavior

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

Orals 7:00-7:20

[7:00]
Hyperparameter Selection for Imitation Learning

Spotlights 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&As 7:50-7:55

[7:50]
Q&A

(ends 8:00 PM)

8 p.m.

Invited Talk:

Xiao Cunde · Qin Dahe

(ends 9:00 PM)

9 p.m.

Posters 9:00-11:00

(ends 11:00 PM)

WED 21 JUL

5 a.m.

Orals 5:00-5:20

[5:00]
Cross-domain Imitation from Observations

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Near Optimal Reward-Free Reinforcement Learning

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
On Energy-Based Models with Overparametrized Shallow Neural Networks

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
APS: Active Pretraining with Successor Features

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets

Spotlights 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&As 5:50-5:55

[5:50]
Q&A

(ends 6:00 AM)

Orals 5:00-5:20

[5:00]
Optimizing persistent homology based functions

Spotlights 5:20-5:50

[5:20]
Debiasing a First-order Heuristic for Approximate Bi-level Optimization