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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.

3:30 p.m.

Lunch -(On Your Own)

5 p.m.

6:30 p.m.

Coffee Only Break

7 p.m.

8 p.m.

Coffee Break

8:30 p.m.

Oral
s
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)

Oral
s
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)

Oral
s
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)

Oral
s
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)

Oral
s
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)

Oral
s
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.

3:30 p.m.

Lunch -(On Your Own)

5 p.m.

Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation

LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation

(ends 6:30 PM)

6:30 p.m.

Coffee Break

7 p.m.

Oral
s
7:00-8:12

[7:00]
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction

[7:08]
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

[7:16]
Whose Opinions Do Language Models Reflect?

[7:24]
A Watermark for Large Language Models

[7:32]
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

[7:40]
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

[7:48]
Inflow, Outflow, and Reciprocity in Machine Learning

[7:56]
Structure-informed Language Models Are Protein Designers

[8:04]
Transformers Learn In-Context by Gradient Descent

(ends 8:30 PM)

Oral
s
7:00-8:12

[7:00]
Pretraining Language Models with Human Preferences

[7:08]
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models

[7:16]
Specializing Smaller Language Models towards Multi-Step Reasoning

[7:24]
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot

[7:32]
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

[7:40]
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

[7:48]
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models

[7:56]
Tractable Control for Autoregressive Language Generation

[8:04]
Equivariant Architectures for Learning in Deep Weight Spaces

(ends 8:30 PM)

Oral
s
7:00-8:20

[7:00]
Nonparametric Extensions of Randomized Response for Private Confidence Sets

[7:08]
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees

[7:16]
Tight Data Access Bounds for Private Top-$k$ Selection

[7:24]
JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift

[7:32]
Active Ranking of Experts Based on their Performances in Many Tasks

[7:40]
The Price of Differential Privacy under Continual Observation

[7:48]
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption

[7:56]
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting

[8:04]
Fast Private Kernel Density Estimation via Locality Sensitive Quantization

[8:12]
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

(ends 8:30 PM)

Oral
s
7:00-8:20

[7:00]
Adversarial Policies Beat Superhuman Go AIs

[7:08]
Adapting to game trees in zero-sum imperfect information games

[7:16]
Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees.

[7:24]
Delving into Noisy Label Detection with Clean Data

[7:32]
Robustly Learning a Single Neuron via Sharpness

[7:40]
Data Feedback Loops: Model-driven Amplification of Dataset Biases

[7:48]
Towards Reliable Neural Specifications

[7:56]
Do Perceptually Aligned Gradients Imply Robustness?

[8:04]
ODS: Test-Time Adaptation in the Presence of Open-World Data Shift

[8:12]
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation

(ends 8:30 PM)

Oral
s
7:00-8:20

[7:00]
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank

[7:08]
Evaluating Self-Supervised Learning via Risk Decomposition

[7:16]
BEATs: Audio Pre-Training with Acoustic Tokenizers

[7:24]
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language

[7:32]
Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions

[7:40]
TRAK: Attributing Model Behavior at Scale

[7:48]
Understanding Plasticity in Neural Networks

[7:56]
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods

[8:04]
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice

[8:12]
Brauer's Group Equivariant Neural Networks

(ends 8:30 PM)

8:45 p.m.

10 p.m.

THU 27 JUL

11 a.m.

(ends 8:00 PM)

11:30 a.m.

12:30 p.m.

Invited Talk:

John Schulman

(ends 1:30 PM)

1 p.m.

Coffee Break

1:30 p.m.

3 p.m.

Lunch -(On Your Own)

4:30 p.m.

6 p.m.

Coffee Only Break

Oral
s
6:00-7:20

[6:00]
Mimetic Initialization of Self-Attention Layers

[6:08]
Difference of submodular minimization via DC programming

[6:16]
Simplex Random Features

[6:24]
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

[6:32]
Tilted Sparse Additive Models

[6:40]
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape

[6:48]
Hyena Hierarchy: Towards Larger Convolutional Language Models

[6:56]
Direct Parameterization of Lipschitz-Bounded Deep Networks

[7:12]
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation

(ends 7:30 PM)

Oral
s
6:00-7:20

[6:00]
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

[6:08]
Self-Interpretable Time Series Prediction with Counterfactual Explanations

[6:16]
Resurrecting Recurrent Neural Networks for Long Sequences

[6:24]
Inferring Relational Potentials in Interacting Systems

[6:32]
Memory-Based Dual Gaussian Processes for Sequential Learning

[6:40]
H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features

[6:48]
Generalized Teacher Forcing for Learning Chaotic Dynamics

[6:56]
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients

[7:04]
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

[7:12]
Learning Control-Oriented Dynamical Structure from Data

(ends 7:30 PM)

Oral
s
6:00-7:20

[6:00]
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding

[6:08]
Calibrating Multimodal Learning

[6:16]
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

[6:24]
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts

[6:32]
Cross-Modal Fine-Tuning: Align then Refine

[6:40]
Mu$^2$SLAM: Multitask, Multilingual Speech and Language Models

[6:48]
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

[6:56]
Pre-training for Speech Translation: CTC Meets Optimal Transport

[7:04]
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models

[7:12]
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes

(ends 7:30 PM)

Oral
s
6:00-7:12

[6:00]
Second-Order Optimization with Lazy Hessians

[6:08]
Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions

[6:16]
Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization

[6:24]
Continuation Path Learning for Homotopy Optimization

[6:32]
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points

[6:40]
Buying Information for Stochastic Optimization

[6:48]
A Fully First-Order Method for Stochastic Bilevel Optimization

[6:56]
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference

[7:04]
Learning-Rate-Free Learning by D-Adaptation

(ends 7:30 PM)

Oral
s
6:00-7:04

[6:00]
Learning Mixtures of Markov Chains and MDPs

[6:08]
Uncertain Evidence in Probabilistic Models and Stochastic Simulators

[6:16]
How Bad is Top-$K$ Recommendation under Competing Content Creators?

[6:24]
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees

[6:32]
Equivariant Polynomials for Graph Neural Networks

[6:40]
Taming graph kernels with random features

[6:48]
Robust Budget Pacing with a Single Sample

[6:56]
Multicalibration as Boosting for Regression

(ends 7:30 PM)

7:30 p.m.

8:45 p.m.

FRI 28 JUL

10 a.m.

11 a.m.

(ends 7:00 PM)

11:50 a.m.

11:55 a.m.

noon

Workshop:

(ends 8:00 PM)

Workshop:

(ends 8:00 PM)

Workshop:

(ends 8:00 PM)

Workshop:

(ends 8:00 PM)

12:15 p.m.

1 p.m.

Coffee Break

3 p.m.

Lunch -(On Your Own)

6 p.m.

Coffee Break

SAT 29 JUL

11 a.m.

(ends 2:00 PM)

11:50 a.m.

11:55 a.m.

noon

Workshop:

(ends 8:15 PM)

12:15 p.m.

1 p.m.

Coffee Break

3 p.m.

Lunch -(On Your Own)

6 p.m.

Coffee Break