Show Detail » Timezone:
Filter Events:  

 

MON 3 MAY
midnight
Remarks:
(ends 1:00 AM)
1 a.m.
(ends 3:00 AM)
3 a.m.
Ozan Sener, Yutian Chen, Blake Richards
Orals 3:00-3:30
[3:00] Dataset Condensation with Gradient Matching
[3:15] Free Lunch for Few-shot Learning: Distribution Calibration
Spotlights 3:30-3:50
[3:30] Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach
[3:40] Generalization in data-driven models of primary visual cortex
Q&As 3:50-4:00
[3:50] Q&A
Orals 4:00-4:30
[4:00] Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
[4:15] A Distributional Approach to Controlled Text Generation
Spotlights 4:30-4:50
[4:30] The Intrinsic Dimension of Images and Its Impact on Learning
[4:40] How Benign is Benign Overfitting ?
Q&As 4:50-5:00
[4:50] Q&A
Orals 5:00-5:45
[5:00] Geometry-aware Instance-reweighted Adversarial Training
[5:15] Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs
[5:30] Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
Spotlights 5:45-5:55
[5:45] Contrastive Divergence Learning is a Time Reversal Adversarial Game
Q&As 5:55-6:05
[5:55] Q&A
(ends 6:05 AM)
6:15 a.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

8 a.m.
Invited Talk:
Timnit Gebru
(ends 9:00 AM)
9 a.m.
(ends 11:00 AM)
11 a.m.
Orals 11:00-11:45
[11:00] Federated Learning Based on Dynamic Regularization
[11:15] Gradient Projection Memory for Continual Learning
[11:30] Growing Efficient Deep Networks by Structured Continuous Sparsification
Spotlights 11:45-11:55
[11:45] Geometry-Aware Gradient Algorithms for Neural Architecture Search
Q&As 11:55-12:05
[11:55] Q&A
Spotlights 12:05-1:05
[12:05] Generalization bounds via distillation
[12:15] On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
[12:25] Sharpness-aware Minimization for Efficiently Improving Generalization
[12:35] Systematic generalisation with group invariant predictions
[12:45] On Statistical Bias In Active Learning: How and When to Fix It
[12:55] Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
Q&As 1:05-1:20
[1:05] Q&A
Spotlights 1:20-2:10
[1:20] Uncertainty Sets for Image Classifiers using Conformal Prediction
[1:30] PMI-Masking: Principled masking of correlated spans
[1:40] Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
[1:50] Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration
[2:00] Predicting Infectiousness for Proactive Contact Tracing
Q&As 2:10-2:23
[2:10] Q&A
(ends 2:23 PM)
2:30 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

4 p.m.
Invited Talk:
Yejin Choi
(ends 5:00 PM)
5 p.m.
(ends 7:00 PM)
7 p.m.
Orals 7:00-7:45
[7:00] SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
[7:15] Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions
[7:30] Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Spotlights 7:45-8:05
[7:45] Structured Prediction as Translation between Augmented Natural Languages
[7:55] Mathematical Reasoning via Self-supervised Skip-tree Training
Q&As 8:05-8:18
[8:05] Q&A
Spotlights 8:18-9:08
[8:18] Improving Adversarial Robustness via Channel-wise Activation Suppressing
[8:28] Fast Geometric Projections for Local Robustness Certification
[8:38] Information Laundering for Model Privacy
[8:48] Dataset Inference: Ownership Resolution in Machine Learning
[8:58] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
Q&As 9:08-9:21
[9:08] Q&A
Orals 9:21-9:36
[9:21] How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Spotlights 9:36-10:06
[9:36] Graph Convolution with Low-rank Learnable Local Filters
[9:46] The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
[9:56] Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Q&As 10:06-10:16
[10:06] Q&A
(ends 10:16 PM)
10:30 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

TUE 4 MAY
midnight
Invited Talk:
Michael Bronstein
(ends 1:00 AM)
1 a.m.
(ends 3:00 AM)
3 a.m.
Orals 3:00-3:15
[3:00] End-to-end Adversarial Text-to-Speech
Spotlights 3:15-3:55
[3:15] Autoregressive Entity Retrieval
[3:25] Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
[3:35] Expressive Power of Invariant and Equivariant Graph Neural Networks
[3:45] Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
Q&As 3:55-4:08
[3:55] Q&A
Orals 4:08-4:38
[4:08] Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
[4:23] Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Spotlights 4:38-4:58
[4:38] Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
[4:48] Noise against noise: stochastic label noise helps combat inherent label noise
Q&As 4:58-5:08
[4:58] Q&A
Spotlights 5:08-5:48
[5:08] Mutual Information State Intrinsic Control
[5:18] Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize
[5:28] Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies
[5:38] Fidelity-based Deep Adiabatic Scheduling
Q&As 5:48-5:58
[5:48] Q&A
(ends 5:58 AM)
6 a.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

8 a.m.
Invited Talk:
Manuela Veloso
(ends 9:00 AM)
9 a.m.
(ends 11:00 AM)
11 a.m.
Orals 11:00-11:30
[11:00] Iterated learning for emergent systematicity in VQA
[11:15] Learning Generalizable Visual Representations via Interactive Gameplay
Spotlights 11:30-11:50
[11:30] How Does Mixup Help With Robustness and Generalization?
[11:40] Recurrent Independent Mechanisms
Q&As 11:50-12:00
[11:50] Q&A
Orals 12:00-12:30
[12:00] Randomized Automatic Differentiation
[12:15] Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Spotlights 12:30-1:00
[12:30] Mind the Pad -- CNNs Can Develop Blind Spots
[12:40] Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time
[12:50] Learning from Protein Structure with Geometric Vector Perceptrons
Q&As 1:00-1:13
[1:00] Q&A
Orals 1:13-1:28
[1:13] On the mapping between Hopfield networks and Restricted Boltzmann Machines
Spotlights 1:28-1:48
[1:28] Learning-based Support Estimation in Sublinear Time
[1:38] Long-tail learning via logit adjustment
Q&As 1:48-1:56
[1:48] Q&A
(ends 1:56 PM)
2 p.m.
3 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

4 p.m.
Town Hall:
(ends 5:00 PM)
5 p.m.
(ends 7:00 PM)
7 p.m.
Orals 7:00-7:15
[7:00] Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Spotlights 7:15-7:45
[7:15] DDPNOpt: Differential Dynamic Programming Neural Optimizer
[7:25] Orthogonalizing Convolutional Layers with the Cayley Transform
[7:35] Model-Based Visual Planning with Self-Supervised Functional Distances
Q&As 7:45-7:55
[7:45] Q&A
Orals 7:55-8:10
[7:55] Global Convergence of Three-layer Neural Networks in the Mean Field Regime
Spotlights 8:10-8:50
[8:10] Minimum Width for Universal Approximation
[8:20] Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
[8:30] Individually Fair Gradient Boosting
[8:40] Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?
Q&As 8:50-9:03
[8:50] Q&A
Orals 9:03-9:33
[9:03] Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
[9:18] MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training
Spotlights 9:33-10:03
[9:33] Locally Free Weight Sharing for Network Width Search
[9:43] Memory Optimization for Deep Networks
[9:53] Neural Topic Model via Optimal Transport
Q&As 10:03-10:16
[10:03] Q&A
(ends 10:16 PM)
10:30 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

WED 5 MAY
midnight
Invited Talk:
Lourdes Agapito
(ends 1:00 AM)
1 a.m.
(ends 3:00 AM)
3 a.m.
Orals 3:00-3:45
[3:00] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
[3:15] Rethinking Attention with Performers
[3:30] Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
Spotlights 3:45-3:55
[3:45] Support-set bottlenecks for video-text representation learning
Q&As 3:55-4:05
[3:55] Q&A
Orals 4:05-4:20
[4:05] Getting a CLUE: A Method for Explaining Uncertainty Estimates
Spotlights 4:20-4:50
[4:20] Influence Estimation for Generative Adversarial Networks
[4:30] Stabilized Medical Image Attacks
[4:40] Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
Q&As 4:50-5:00
[4:50] Q&A
Orals 5:00-5:15
[5:00] Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
Spotlights 5:15-5:55
[5:15] Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
[5:25] Tent: Fully Test-Time Adaptation by Entropy Minimization
[5:35] Neural Approximate Sufficient Statistics for Implicit Models
[5:45] Implicit Normalizing Flows
Q&As 5:55-6:08
[5:55] Q&A
(ends 6:08 AM)
6:15 a.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

8 a.m.
Invited Talk:
Kate Saenko
(ends 9:00 AM)
9 a.m.
(ends 11:00 AM)
11 a.m.
Orals 11:00-12:00
[11:00] Human-Level Performance in No-Press Diplomacy via Equilibrium Search
[11:15] Learning to Reach Goals via Iterated Supervised Learning
[11:30] Learning Invariant Representations for Reinforcement Learning without Reconstruction
[11:45] Evolving Reinforcement Learning Algorithms
Spotlights 12:00-12:10
[12:00] Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
Q&As 12:10-12:23
[12:10] Q&A
Orals 12:23-12:38
[12:23] Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
Spotlights 12:38-1:08
[12:38] Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
[12:48] LambdaNetworks: Modeling long-range Interactions without Attention
[12:58] Grounded Language Learning Fast and Slow
Q&As 1:08-1:18
[1:08] Q&A
Spotlights 1:18-2:08
[1:18] Unsupervised Object Keypoint Learning using Local Spatial Predictability
[1:28] VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
[1:38] Dynamic Tensor Rematerialization
[1:48] A Gradient Flow Framework For Analyzing Network Pruning
[1:58] Differentially Private Learning Needs Better Features (or Much More Data)
Q&As 2:08-2:21
[2:08] Q&A
(ends 2:21 PM)
3 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

4 p.m.
Orals 4:00-4:45
[4:00] Neural Synthesis of Binaural Speech From Mono Audio
[4:15] EigenGame: PCA as a Nash Equilibrium
[4:30] Score-Based Generative Modeling through Stochastic Differential Equations
Spotlights 4:45-4:55
[4:45] Learning Mesh-Based Simulation with Graph Networks
Q&As 4:55-5:05
[4:55] Q&A
(ends 5:05 PM)
5 p.m.
(ends 7:00 PM)
7 p.m.
Orals 7:00-7:15
[7:00] Improved Autoregressive Modeling with Distribution Smoothing
Spotlights 7:15-7:45
[7:15] GAN "Steerability" without optimization
[7:25] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
[7:35] Emergent Symbols through Binding in External Memory
Q&As 7:45-7:55
[7:45] Q&A
Orals 7:55-8:10
[7:55] Deformable DETR: Deformable Transformers for End-to-End Object Detection
Spotlights 8:10-9:00
[8:10] Graph-Based Continual Learning
[8:20] Understanding the role of importance weighting for deep learning
[8:30] Towards Robustness Against Natural Language Word Substitutions
[8:40] Undistillable: Making A Nasty Teacher That CANNOT teach students
[8:50] CPT: Efficient Deep Neural Network Training via Cyclic Precision
Q&As 9:00-9:15
[9:00] Q&A
Spotlights 9:15-9:55
[9:15] PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
[9:25] Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
[9:35] Regularized Inverse Reinforcement Learning
[9:45] Behavioral Cloning from Noisy Demonstrations
Q&As 9:55-10:05
[9:55] Q&A
(ends 10:05 PM)
10:15 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

THU 6 MAY
midnight
Orals 12:00-12:45
[12:00] Rethinking Architecture Selection in Differentiable NAS
[12:15] Complex Query Answering with Neural Link Predictors
[12:30] Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
Spotlights 12:45-12:55
[12:45] Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
Q&As 12:55-1:05
[12:55] Q&A
(ends 1:05 AM)
1 a.m.
(ends 3:00 AM)
3 a.m.
Orals 3:00-3:15
[3:00] What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
Spotlights 3:15-4:05
[3:15] Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
[3:25] UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
[3:35] Quantifying Differences in Reward Functions
[3:45] Iterative Empirical Game Solving via Single Policy Best Response
[3:55] Discovering a set of policies for the worst case reward
Q&As 4:05-4:20
[4:05] Q&A
Orals 4:20-4:35
[4:20] Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Spotlights 4:35-5:25
[4:35] Unlearnable Examples: Making Personal Data Unexploitable
[4:45] Self-supervised Visual Reinforcement Learning with Object-centric Representations
[4:55] On Self-Supervised Image Representations for GAN Evaluation
[5:05] Retrieval-Augmented Generation for Code Summarization via Hybrid GNN
[5:15] Practical Real Time Recurrent Learning with a Sparse Approximation
Q&As 5:25-5:40
[5:25] Q&A
(ends 5:40 AM)
6 a.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

8 a.m.
9 a.m.
(ends 11:00 AM)
11 a.m.
Orals 11:00-12:00
[11:00] VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments
[11:15] SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
[11:30] When Do Curricula Work?
[11:45] Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
Q&As 12:00-12:10
[12:00] Q&A
Spotlights 12:10-12:50
[12:10] Correcting experience replay for multi-agent communication
[12:20] Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
[12:30] DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs
[12:40] Data-Efficient Reinforcement Learning with Self-Predictive Representations
Q&As 12:50-1:00
[12:50] Q&A
Orals 1:00-1:30
[1:00] DiffWave: A Versatile Diffusion Model for Audio Synthesis
[1:15] Self-training For Few-shot Transfer Across Extreme Task Differences
Spotlights 1:30-2:00
[1:30] A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
[1:40] BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
[1:50] Disentangled Recurrent Wasserstein Autoencoder
Q&As 2:00-2:13
[2:00] Q&A
(ends 2:13 PM)
2 p.m.
Expo Talk Panel:
(ends 3:00 PM)
3 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

4 p.m.
Invited Talk:
Alyosha Efros
(ends 5:00 PM)
5 p.m.
(ends 7:00 PM)
7 p.m.
Orals 7:00-7:15
[7:00] Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Spotlights 7:15-7:45
[7:15] Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
[7:25] Self-Supervised Policy Adaptation during Deployment
[7:35] What are the Statistical Limits of Offline RL with Linear Function Approximation?
Q&As 7:45-7:55
[7:45] Q&A
Spotlights 7:55-8:45
[7:55] RMSprop converges with proper hyper-parameter
[8:05] A Good Image Generator Is What You Need for High-Resolution Video Synthesis
[8:15] Random Feature Attention
[8:25] Learning with Feature-Dependent Label Noise: A Progressive Approach
[8:35] Sparse Quantized Spectral Clustering
Q&As 8:45-8:58
[8:45] Q&A
Spotlights 8:58-9:38
[8:58] Learning a Latent Simplex in Input Sparsity Time
[9:08] Topology-Aware Segmentation Using Discrete Morse Theory
[9:18] MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
[9:28] Distributional Sliced-Wasserstein and Applications to Generative Modeling
Q&As 9:38-9:48
[9:38] Q&A
(ends 9:48 PM)
10 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

Remarks:
(ends 11:00 PM)
FRI 7 MAY
2:30 a.m.
Workshop:
(ends 11:30 AM)
4:45 a.m.
5:45 a.m.
5:55 a.m.
Workshop:
(ends 3:00 PM)
6 a.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.

Workshop:
(ends 1:55 PM)
Workshop:
(ends 3:00 PM)
6:30 a.m.
6:45 a.m.
Workshop:
(ends 7:00 PM)
8:30 a.m.
8:45 a.m.
Workshop:
(ends 5:00 PM)
2 p.m.
BREAK:

Please visit the Sponsor Hall, the Socials, and Mentorships.