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Timezone: America/Los_Angeles |

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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)

5 p.m.

Exhibit Hall Open

5:30 p.m.

Coffee Only Break 310, 320

6 p.m.

7 p.m.

Coffee Break - Exhibit Hall 3

8 p.m.

MON 24 JUL

11 a.m.

(ends 10:00 PM)

11:30 a.m.

11:45 a.m.

12:30 p.m.

Tutorial:

(ends 3:00 PM)

1 p.m.

Exhibit Hall Open

1:30 p.m.

Coffee Break

3 p.m.

Lunch -(On Your Own)

4:30 p.m.

Tutorial:

(ends 6:30 PM)

Tutorial:

(ends 6:30 PM)

Tutorial:

(ends 6:30 PM)

6:30 p.m.

Coffee Break

7 p.m.

Tutorial:

(ends 9:00 PM)

9:15 p.m.

9:30 p.m.

TUE 25 JUL

11 a.m.

(ends 9:00 PM)

noon

12:15 p.m.

1 p.m.

Exhibit Hall Open

1:30 p.m.

Coffee Break

2 p.m.

(ends 3:30 PM)

3:30 p.m.

Lunch -(On Your Own)

5 p.m.

(ends 6:30 PM)

6:30 p.m.

Coffee Only Break

7 p.m.

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.