Skip to yearly menu bar Skip to main content


Show Detail Timezone:
America/Los_Angeles
 
Filter Rooms:  

 

SUN 23 JUL
1 p.m.
(ends 8:00 PM)
2:30 p.m.
Lunch (On Your Own)
3:30 p.m.
Expo Talk Panel with Coffee & a Snack:
(ends 4:30 PM)
4:30 p.m.
Expo Talk Panel with Coffee & a Snack:
(ends 5:30 PM)
Expo Talk Panel:
(ends 5:30 PM)
5 p.m.
Exhibit Hall Open
5:30 p.m.
Coffee Only Break 310, 320
6 p.m.
Expo Talk Panel:
(ends 7:00 PM)
7 p.m.
Coffee Break - Exhibit Hall 3
8 p.m.
Expo Talk Panel:
(ends 9:00 PM)
MON 24 JUL
11 a.m.
(ends 10:00 PM)
11:30 a.m.
Panel:
(ends 12:30 PM)
11:45 a.m.
Affinity Workshop:
(ends 8:00 PM)
1 p.m.
Exhibit Hall Open
1:30 p.m.
Coffee Break
3 p.m.
Lunch -(On Your Own)
6:30 p.m.
Coffee Break
9:15 p.m.
Welcome Reception:
(ends 11:00 PM)
9:30 p.m.
EXPO Attendee Raffle Prize Give Away:
(ends 9:45 PM)
TUE 25 JUL
11 a.m.
(ends 9:00 PM)
noon
Opening Remarks:
(ends 12:15 PM)
12:15 p.m.
Invited Talk:
Marzyeh Ghassemi
(ends 1:30 PM)
1 p.m.
Exhibit Hall Open
1:30 p.m.
Coffee Break
2 p.m.
Posters 2:00-4:30
(ends 3:30 PM)
3:30 p.m.
Lunch -(On Your Own)
5 p.m.
Posters 5:00-6:30
(ends 6:30 PM)
6:30 p.m.
Coffee Only Break
7 p.m.
Invited Talk:
Shakir Mohamed
(ends 8:00 PM)
8 p.m.
Coffee Break
8:30 p.m.
Orals 8:30-9:50
[8:30] Bayesian Design Principles for Frequentist Sequential Learning
[8:38] Towards Theoretical Understanding of Inverse Reinforcement Learning
[8:46] On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
[8:54] Delayed Feedback in Kernel Bandits
[9:02] Provably Learning Object-Centric Representations
[9:10] Task-specific experimental design for treatment effect estimation
[9:18] Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.
[9:26] Interventional Causal Representation Learning
[9:34] Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
[9:42] Sequential Underspecified Instrument Selection for Cause-Effect Estimation
(ends 10:00 PM)
Orals 8:30-9:50
[8:30] Raising the Cost of Malicious AI-Powered Image Editing
[8:38] Dynamics-inspired Neuromorphic Visual Representation Learning
[8:46] Scaling Vision Transformers to 22 Billion Parameters
[8:54] Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction
[9:02] Fourmer: An Efficient Global Modeling Paradigm for Image Restoration
[9:10] Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
[9:18] Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
[9:26] Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
[9:34] SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
[9:42] Fast Inference from Transformers via Speculative Decoding
(ends 10:00 PM)
Orals 8:30-9:58
[8:30] Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
[8:38] Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond
[8:46] Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
[8:54] Tighter Information-Theoretic Generalization Bounds from Supersamples
[9:02] Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
[9:10] Bayes-optimal Learning of Deep Random Networks of Extensive-width
[9:18] Why does Throwing Away Data Improve Worst-Group Error?
[9:26] Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
[9:34] Sharper Bounds for $\ell_p$ Sensitivity Sampling
[9:42] AdaBoost is not an Optimal Weak to Strong Learner
[9:50] Generalization on the Unseen, Logic Reasoning and Degree Curriculum
(ends 10:00 PM)
Orals 8:30-9:50
[8:30] AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
[8:38] Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
[8:46] Graphically Structured Diffusion Models
[8:54] Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
[9:02] Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
[9:10] Diffusion Models are Minimax Optimal Distribution Estimators
[9:18] GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
[9:26] OCD: Learning to Overfit with Conditional Diffusion Models
[9:34] Denoising MCMC for Accelerating Diffusion-Based Generative Models
[9:42] Cones: Concept Neurons in Diffusion Models for Customized Generation
(ends 10:00 PM)
Orals 8:30-9:50
[8:30] Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark
[8:38] Information-Theoretic State Space Model for Multi-View Reinforcement Learning
[8:46] Reparameterized Policy Learning for Multimodal Trajectory Optimization
[8:54] Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
[9:02] Subequivariant Graph Reinforcement Learning in 3D Environments
[9:10] A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
[9:18] Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
[9:26] Efficient RL via Disentangled Environment and Agent Representations
[9:34] Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
[9:42] On the Statistical Benefits of Temporal Difference Learning
(ends 10:00 PM)
Orals 8:30-9:50
[8:30] Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
[8:38] The Dormant Neuron Phenomenon in Deep Reinforcement Learning
[8:46] Reinforcement Learning from Passive Data via Latent Intentions
[8:54] Best of Both Worlds Policy Optimization
[9:02] Exponential Smoothing for Off-Policy Learning
[9:10] Quantile Credit Assignment
[9:18] Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
[9:26] Hierarchies of Reward Machines
[9:34] Human-Timescale Adaptation in an Open-Ended Task Space
[9:42] Settling the Reward Hypothesis
(ends 10:00 PM)
WED 26 JUL
11 a.m.
(ends 9:00 PM)
12:30 p.m.
Invited Talk:
Jennifer Doudna
(ends 1:30 PM)
1 p.m.
Exhibit Hall Open
1:30 p.m.
Coffee Break
2 p.m.
Posters 2:00-3:30