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SUN 6 DEC

5 a.m.

6 a.m.

Expo Talk Panel:

(ends 7:00 AM)

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.

11 a.m.

noon

Expo Demonstration:

(ends 1:00 PM)

1 p.m.

Expo Talk Panel:

(ends 2:00 PM)

2 p.m.

Expo Talk Panel:

(ends 3:00 PM)

3 p.m.

4 p.m.

5 p.m.

Expo Talk Panel:

(ends 6:00 PM)

6 p.m.

Expo Demonstration:

(ends 7:00 PM)

7 p.m.

Expo Demonstration:

(ends 8:00 PM)

8 p.m.

9 p.m.

Expo Demonstration:

(ends 10:00 PM)

MON 7 DEC

midnight

2:30 a.m.

3 a.m.

5:30 a.m.

Tutorial:

(ends 8:00 AM)

6 a.m.

8 a.m.

Tutorial:

(ends 10:30 AM)

Tutorial:

(ends 10:30 AM)

11 a.m.

Tutorial:

(ends 1:30 PM)

12:30 p.m.

1:30 p.m.

Tutorial:

(ends 4:00 PM)

Tutorial:

(ends 4:00 PM)

5 p.m.

Invited Talk:

Charles Isbell

(ends 7:00 PM)

6 p.m.

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Learning Physical Graph Representations from Visual Scenes

[6:15]
Multi-label Contrastive Predictive Coding

[6:30]
Equivariant Networks for Hierarchical Structures

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
On the Equivalence between Online and Private Learnability beyond Binary Classification

[7:10]
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

[7:20]
Joint Contrastive Learning with Infinite Possibilities

[7:30]
Neural Methods for Point-wise Dependency Estimation

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Design Space for Graph Neural Networks

[8:00]
Debiased Contrastive Learning

[8:10]
The Autoencoding Variational Autoencoder

[8:20]
Unsupervised Representation Learning by Invariance Propagation

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 PM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

[6:15]
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

[6:30]
Neural encoding with visual attention

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

[7:10]
Using noise to probe recurrent neural network structure and prune synapses

[7:20]
Interpretable Sequence Learning for Covid-19 Forecasting

[7:30]
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:20

[7:50]
Demixed shared component analysis of neural population data from multiple brain areas

[8:00]
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects

[8:10]
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models

Q&A
s
8:20-8:30

[8:20]
Joint Q&A for Preceeding Spotlights

Break
s
8:30-9:00

(ends 9:00 PM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Language Models are Few-Shot Learners

[6:15]
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

[6:30]
The Cone of Silence: Speech Separation by Localization

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Unsupervised Sound Separation Using Mixture Invariant Training

[7:10]
Investigating Gender Bias in Language Models Using Causal Mediation Analysis

[7:20]
A Simple Language Model for Task-Oriented Dialogue

[7:30]
ConvBERT: Improving BERT with Span-based Dynamic Convolution

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Cross-lingual Retrieval for Iterative Self-Supervised Training

[8:00]
DynaBERT: Dynamic BERT with Adaptive Width and Depth

[8:10]
Incorporating Pragmatic Reasoning Communication into Emergent Language

[8:20]
De-Anonymizing Text by Fingerprinting Language Generation

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 PM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search

[6:15]
Novelty Search in Representational Space for Sample Efficient Exploration

[6:30]
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
First Order Constrained Optimization in Policy Space

[7:10]
CoinDICE: Off-Policy Confidence Interval Estimation

[7:20]
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

[7:30]
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning

[8:00]
Bayesian Multi-type Mean Field Multi-agent Imitation Learning

[8:10]
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

[8:20]
Safe Reinforcement Learning via Curriculum Induction

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 PM)

9 p.m.

(ends 11:00 PM)

TUE 8 DEC

2 a.m.

3 a.m.

5 a.m.

6 a.m.

Tue demos repeat on Wed

Demonstration
s
6:00-9:20

(duration 3.3 hr)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Exact Recovery of Mangled Clusters with Same-Cluster Queries

[6:15]
Deep Transformation-Invariant Clustering

[6:30]
Partially View-aligned Clustering

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Simple and Scalable Sparse k-means Clustering via Feature Ranking

[7:10]
Simultaneous Preference and Metric Learning from Paired Comparisons

[7:20]
Learning Optimal Representations with the Decodable Information Bottleneck

[7:30]
Manifold structure in graph embeddings

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:20

[7:50]
Self-Supervised Learning by Cross-Modal Audio-Video Clustering

[8:00]
Classification with Valid and Adaptive Coverage

[8:10]
On ranking via sorting by estimated expected utility

Q&A
s
8:20-8:30

[8:20]
Joint Q&A for Preceeding Spotlights

Break
s
8:30-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Deep Energy-based Modeling of Discrete-Time Physics

[6:15]
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory

[6:30]
Dissecting Neural ODEs

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Robust Density Estimation under Besov IPM Losses

[7:10]
Almost Surely Stable Deep Dynamics

[7:20]
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks

[7:30]
A Theoretical Framework for Target Propagation

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Training Generative Adversarial Networks by Solving Ordinary Differential Equations

[8:00]
Information theoretic limits of learning a sparse rule

[8:10]
Constant-Expansion Suffices for Compressed Sensing with Generative Priors

[8:20]
Logarithmic Pruning is All You Need

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

[6:15]
Causal Intervention for Weakly-Supervised Semantic Segmentation

[6:30]
Convolutional Generation of Textured 3D Meshes

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
DISK: Learning local features with policy gradient

[7:10]
Wasserstein Distances for Stereo Disparity Estimation

[7:20]
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance

[7:30]
Learning Semantic-aware Normalization for Generative Adversarial Networks

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Neural Sparse Voxel Fields

[8:00]
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data

[8:10]
Learning to Detect Objects with a 1 Megapixel Event Camera

[8:20]
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Multiscale Deep Equilibrium Models

[6:15]
On the Modularity of Hypernetworks

[6:30]
Training Generative Adversarial Networks with Limited Data

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
MeshSDF: Differentiable Iso-Surface Extraction

[7:10]
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

[7:20]
Monotone operator equilibrium networks

[7:30]
What Do Neural Networks Learn When Trained With Random Labels?

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

[8:00]
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks

[8:10]
The phase diagram of approximation rates for deep neural networks

[8:20]
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

[6:15]
Escaping the Gravitational Pull of Softmax

[6:30]
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Interferobot: aligning an optical interferometer by a reinforcement learning agent

[7:10]
On Efficiency in Hierarchical Reinforcement Learning

[7:20]
Finite-Time Analysis for Double Q-learning

[7:30]
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

[8:00]
Model-based Policy Optimization with Unsupervised Model Adaptation

[8:10]
Variational Policy Gradient Method for Reinforcement Learning with General Utilities

[8:20]
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Adversarially Robust Streaming Algorithms via Differential Privacy

[6:15]
Differentially Private Clustering: Tight Approximation Ratios

[6:30]
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

[7:10]
Private Identity Testing for High-Dimensional Distributions

[7:20]
Permute-and-Flip: A new mechanism for differentially private selection

[7:30]
Smoothed Analysis of Online and Differentially Private Learning

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:20

[7:50]
Optimal Private Median Estimation under Minimal Distributional Assumptions

[8:00]
Assisted Learning: A Framework for Multi-Organization Learning

[8:10]
Higher-Order Certification For Randomized Smoothing

Q&A
s
8:20-8:30

[8:20]
Joint Q&A for Preceeding Spotlights

Break
s
8:30-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium

[6:15]
Efficient active learning of sparse halfspaces with arbitrary bounded noise

[6:30]
Learning Parities with Neural Networks

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation

[7:10]
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics

[7:20]
A Bandit Learning Algorithm and Applications to Auction Design

[7:30]
An Optimal Elimination Algorithm for Learning a Best Arm

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote

[8:00]
PAC-Bayesian Bound for the Conditional Value at Risk

[8:10]
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability

[8:20]
Hedging in games: Faster convergence of external and swap regrets

[8:30]
Online Bayesian Persuasion

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 AM)

7 a.m.

7:30 a.m.

9 a.m.

noon

Tutorial:

(ends 12:50 PM)

1 p.m.

2 p.m.

Tutorial:

(ends 2:50 PM)

5 p.m.

Invited Talk:

Shafi Goldwasser

(ends 7:00 PM)

6 p.m.

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Space-Time Correspondence as a Contrastive Random Walk

[6:15]
Rethinking Pre-training and Self-training

[6:30]
Do Adversarially Robust ImageNet Models Transfer Better?

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Self-Supervised Visual Representation Learning from Hierarchical Grouping

[7:10]
Learning Affordance Landscapes for Interaction Exploration in 3D Environments

[7:20]
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D

[7:30]
Large-Scale Adversarial Training for Vision-and-Language Representation Learning

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:20

[7:50]
Measuring Robustness to Natural Distribution Shifts in Image Classification

[8:00]
Curriculum By Smoothing

[8:10]
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies

Q&A
s
8:20-8:30

[8:20]
Joint Q&A for Preceeding Spotlights

Break
s
8:30-9:00

(ends 9:00 PM)

Tue demos repeat on Wed

Demonstration
s
6:00-9:20

(duration 3.3 hr)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Implicit Neural Representations with Periodic Activation Functions

[6:15]
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

[6:30]
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures

[7:10]
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization

[7:20]
Compositional Visual Generation with Energy Based Models

[7:30]
Certified Monotonic Neural Networks

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing

[8:00]
On Correctness of Automatic Differentiation for Non-Differentiable Functions

[8:10]
The Complete Lasso Tradeoff Diagram

[8:20]
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 PM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

[6:15]
Learning Individually Inferred Communication for Multi-Agent Cooperation

[6:30]
Can Temporal-Diﬀerence and Q-Learning Learn Representation? A Mean-Field Theory

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Reinforcement Learning with Augmented Data

[7:10]
Sub-sampling for Efficient Non-Parametric Bandit Exploration

[7:20]
Language-Conditioned Imitation Learning for Robot Manipulation Tasks

[7:30]
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Policy Improvement via Imitation of Multiple Oracles

[8:00]
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

[8:10]
Avoiding Side Effects in Complex Environments

[8:20]
Preference-based Reinforcement Learning with Finite-Time Guarantees

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 PM)

9 p.m.

(ends 11:00 PM)

WED 9 DEC

1 a.m.

1:40 a.m.

3 a.m.

5 a.m.

6 a.m.

Tue demos repeat on Wed

Demonstration
s
6:00-9:20

(duration 3.3 hr)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
High-Fidelity Generative Image Compression

[6:15]
Learning Composable Energy Surrogates for PDE Order Reduction

[6:30]
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Compositional Generalization by Learning Analytical Expressions

[7:10]
Modern Hopfield Networks and Attention for Immune Repertoire Classification

[7:20]
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

[7:30]
A causal view of compositional zero-shot recognition

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

[8:00]
Barking up the right tree: an approach to search over molecule synthesis DAGs

[8:10]
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views

[8:20]
Experimental design for MRI by greedy policy search

[8:30]
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Continual Deep Learning by Functional Regularisation of Memorable Past

[6:15]
Look-ahead Meta Learning for Continual Learning

[6:30]
NeuMiss networks: differentiable programming for supervised learning with missing values.

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Meta-trained agents implement Bayes-optimal agents

[7:10]
Linear Dynamical Systems as a Core Computational Primitive

[7:20]
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

[7:30]
Uncertainty-aware Self-training for Few-shot Text Classification

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
HiPPO: Recurrent Memory with Optimal Polynomial Projections

[8:00]
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity

[8:10]
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

[8:20]
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

[8:30]
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces

[6:15]
Kernel Methods Through the Roof: Handling Billions of Points Efficiently

[6:30]
A Group-Theoretic Framework for Data Augmentation

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
A mathematical model for automatic differentiation in machine learning

[7:10]
A kernel test for quasi-independence

[7:20]
Fourier Sparse Leverage Scores and Approximate Kernel Learning

[7:30]
BOSS: Bayesian Optimization over String Spaces

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
Fast geometric learning with symbolic matrices

[8:00]
Training Stronger Baselines for Learning to Optimize

[8:10]
Learning Linear Programs from Optimal Decisions

[8:20]
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Ultra-Low Precision 4-bit Training of Deep Neural Networks

[6:15]
Reservoir Computing meets Recurrent Kernels and Structured Transforms

[6:30]
The interplay between randomness and structure during learning in RNNs

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
What if Neural Networks had SVDs?

[7:10]
Practical Quasi-Newton Methods for Training Deep Neural Networks

[7:20]
Triple descent and the two kinds of overfitting: where & why do they appear?

[7:30]
On the linearity of large non-linear models: when and why the tangent kernel is constant

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:20

[7:50]
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

[8:00]
Proximal Mapping for Deep Regularization

[8:10]
BoxE: A Box Embedding Model for Knowledge Base Completion

Q&A
s
8:20-8:30

[8:20]
Joint Q&A for Preceeding Spotlights

Break
s
8:30-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Network-to-Network Translation with Conditional Invertible Neural Networks

[6:15]
Causal Imitation Learning With Unobserved Confounders

[6:30]
Gradient Estimation with Stochastic Softmax Tricks

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

[7:10]
A Randomized Algorithm to Reduce the Support of Discrete Measures

[7:20]
A/B Testing in Dense Large-Scale Networks: Design and Inference

[7:30]
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift

[8:00]
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

[8:10]
Differentiable Causal Discovery from Interventional Data

[8:20]
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks

[8:30]
Efficient semidefinite-programming-based inference for binary and multi-class MRFs

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

[6:15]
Metric-Free Individual Fairness in Online Learning

[6:30]
Fair regression via plug-in estimator and recalibration with statistical guarantees

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay

[7:10]
Differentially-Private Federated Linear Bandits

[7:20]
Adversarial Training is a Form of Data-dependent Operator Norm Regularization

[7:30]
Prediction with Corrupted Expert Advice

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

[8:00]
Towards Safe Policy Improvement for Non-Stationary MDPs

[8:10]
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

[8:20]
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

[8:30]
Understanding Gradient Clipping in Private SGD: A Geometric Perspective

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 AM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-7:00

[6:00]
Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

[6:15]
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form

[6:30]
Acceleration with a Ball Optimization Oracle

[6:45]
Convex optimization based on global lower second-order models

Spotlight
s
7:00-7:40

[7:00]
Adam with Bandit Sampling for Deep Learning

[7:10]
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling

[7:20]
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method

[7:30]
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
Minibatch Stochastic Approximate Proximal Point Methods

[8:00]
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems

[8:10]
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms

[8:20]
Linearly Converging Error Compensated SGD

[8:30]
Learning Augmented Energy Minimization via Speed Scaling

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 AM)

9 a.m.

11 a.m.

noon

Tutorial:

(ends 12:50 PM)

2 p.m.

5 p.m.

6 p.m.

Tue demos repeat on Wed

Demonstration
s
6:00-9:20

(duration 3.3 hr)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

[6:15]
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

[6:30]
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Distribution Matching for Crowd Counting

[7:10]
Texture Interpolation for Probing Visual Perception

[7:20]
Consistent Structural Relation Learning for Zero-Shot Segmentation

[7:30]
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:30

[7:50]
ShapeFlow: Learnable Deformation Flows Among 3D Shapes

[8:00]
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows

[8:10]
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen

[8:20]
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

Q&A
s
8:30-8:40

[8:30]
Joint Q&A for Preceeding Spotlights

Break
s
8:40-9:00

(ends 9:00 PM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
FrugalML: How to use ML Prediction APIs more accurately and cheaply

[6:15]
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

[6:30]
PyGlove: Symbolic Programming for Automated Machine Learning

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
Improved Schemes for Episodic Memory-based Lifelong Learning

[7:10]
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

[7:20]
Uncertainty Aware Semi-Supervised Learning on Graph Data

[7:30]
Rethinking Importance Weighting for Deep Learning under Distribution Shift

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:20

[7:50]
Modular Meta-Learning with Shrinkage

[8:00]
JAX MD: A Framework for Differentiable Physics

[8:10]
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Q&A
s
8:20-8:30

[8:20]
Joint Q&A for Preceeding Spotlights

Break
s
8:30-9:00

(ends 9:00 PM)

Each Oral includes Q&A

Spotlights have joint Q&As

Spotlights have joint Q&As

Oral
s
6:00-6:45

[6:00]
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method

[6:15]
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs

[6:30]
Worst-Case Analysis for Randomly Collected Data

Break
s
6:45-7:00

Spotlight
s
7:00-7:40

[7:00]
On Adaptive Distance Estimation

[7:10]
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits

[7:20]
Delay and Cooperation in Nonstochastic Linear Bandits

[7:30]
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

Q&A
s
7:40-7:50

[7:40]
Joint Q&A for Preceeding Spotlights

Spotlight
s
7:50-8:40

[7:50]
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition

[8:00]
A Tight Lower Bound and Efficient Reduction for Swap Regret

[8:10]
Estimation of Skill Distribution from a Tournament

[8:20]
Optimal Prediction of the Number of Unseen Species with Multiplicity

[8:30]
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks

Q&A
s
8:40-8:50

[8:40]
Joint Q&A for Preceeding Spotlights

Break
s
8:50-9:00

(ends 9:00 PM)

THU 10 DEC

midnight

2 a.m.

3 a.m.

Tutorial:

(ends 3:50 AM)

4 a.m.

Town Hall: