Show Detail » Timezone:
Filter Events:  

 

SUN 5 DEC
4 p.m.
MON 6 DEC
TUE 7 DEC
midnight
Datasets and Benchmarks:
(ends 2:00 AM)
Orals 12:00-12:15
[12:00] MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Q&As 12:15-12:20
[12:15] Q&A
Orals 12:20-12:35
[12:20] Learning to Draw: Emergent Communication through Sketching
Q&As 12:35-12:40
[12:35] Q&A
Orals 12:40-12:55
[12:40] Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Q&As 12:55-1:00
[12:55] Q&A
(ends 1:00 AM)
Orals 12:00-12:15
[12:00] Framing RNN as a kernel method: A neural ODE approach
Q&As 12:15-12:20
[12:15] Q&A
Orals 12:20-12:35
[12:20] A Universal Law of Robustness via Isoperimetry
Q&As 12:35-12:40
[12:35] Q&A
Orals 12:40-12:55
[12:40] Causal Identification with Matrix Equations
Q&As 12:55-1:00
[12:55] Q&A
(ends 1:00 AM)
Orals 12:00-12:15
[12:00] E(n) Equivariant Normalizing Flows
Q&As 12:15-12:20
[12:15] Q&A
Orals 12:20-12:35
[12:20] Online Variational Filtering and Parameter Learning
Q&As 12:35-12:40
[12:35] Q&A
Orals 12:40-12:55
[12:40] Alias-Free Generative Adversarial Networks
Q&As 12:55-1:00
[12:55] Q&A
(ends 1:00 AM)
Orals 12:00-12:15
[12:00] Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Q&As 12:15-12:20
[12:15] Q&A
Orals 12:20-12:35
[12:20] Near-Optimal No-Regret Learning in General Games
Q&As 12:35-12:40
[12:35] Q&A
Orals 12:40-12:55
[12:40] Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
Q&As 12:55-1:00
[12:55] Q&A
(ends 1:00 AM)
1 a.m.
Orals 1:00-1:15
[1:00] Attention over Learned Object Embeddings Enables Complex Visual Reasoning
Q&As 1:15-1:20
[1:15] Q&A
Orals 1:20-1:35
[1:20] Learning Frequency Domain Approximation for Binary Neural Networks
Q&As 1:35-1:40
[1:35] Q&A
Orals 1:40-1:55
[1:40] Learning Debiased Representation via Disentangled Feature Augmentation
Q&As 1:55-2:00
[1:55] Q&A
(ends 2:00 AM)
Orals 1:00-1:15
[1:00] EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
Q&As 1:15-1:20
[1:15] Q&A
Orals 1:20-1:35
[1:20] Differentiable Quality Diversity
Q&As 1:35-1:40
[1:35] Q&A
Orals 1:40-1:55
[1:40] Hessian Eigenspectra of More Realistic Nonlinear Models
Q&As 1:55-2:00
[1:55] Q&A
(ends 2:00 AM)
Orals 1:00-1:15
[1:00] An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap
Q&As 1:15-1:20
[1:15] Q&A
Orals 1:20-1:35
[1:20] On the Expressivity of Markov Reward
Q&As 1:35-1:40
[1:35] Q&A
Orals 1:40-1:55
[1:40] The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
Q&As 1:55-2:00
[1:55] Q&A
(ends 2:00 AM)
Orals 1:00-1:15
[1:00] Data driven semi-supervised learning
Q&As 1:15-1:20
[1:15] Q&A
Orals 1:20-1:35
[1:20] Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$
Q&As 1:35-1:40
[1:35] Q&A
Orals 1:40-1:55
[1:40] The Complexity of Bayesian Network Learning: Revisiting the Superstructure
Q&As 1:55-2:00
[1:55] Q&A
(ends 2:00 AM)
5 a.m.
Affinity Workshop:
(ends 7:00 AM)
6:02 a.m.
Affinity Workshop:
(ends 6:50 AM)
7 a.m.
Invited Talk:
Luis von Ahn
(ends 8:30 AM)
8:30 a.m.
Demonstration:
(ends 9:50 AM)
Datasets and Benchmarks:
(ends 10:00 AM)
(ends 10:00 AM)
10 a.m.
Competition:
(ends 12:05 PM)
Affinity Workshop:
(ends 3:00 PM)
11 a.m.
Affinity Workshop:
(ends 2:33 PM)
4:30 p.m.
(ends 6:00 PM)
6 p.m.
11 p.m.
Invited Talk (Breiman Lecture):
Gabor Lugosi
(ends 12:30 AM)
WED 8 DEC
midnight
Datasets and Benchmarks:
(ends 2:00 AM)
12:30 a.m.
(ends 2:00 AM)
2 a.m.
Competition:
(ends 3:45 AM)
3 a.m.
8 a.m.
Datasets and Benchmarks:
(ends 9:00 AM)
Orals 8:00-8:15
[8:00] Unsupervised Speech Recognition
Q&As 8:15-8:20
[8:15] Q&A
Orals 8:20-8:35
[8:20] Deep Reinforcement Learning at the Edge of the Statistical Precipice
Q&As 8:35-8:40
[8:35] Q&A
Orals 8:40-8:55
[8:40] Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
Q&As 8:55-9:00
[8:55] Q&A
(ends 9:00 AM)
Orals 8:00-8:15
[8:00] Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
Q&As 8:15-8:20
[8:15] Q&A
Orals 8:20-8:35
[8:20] Oracle Complexity in Nonsmooth Nonconvex Optimization
Q&As 8:35-8:40
[8:35] Q&A
Orals 8:40-8:55
[8:40] Faster Matchings via Learned Duals
Q&As 8:55-9:00
[8:55] Q&A
(ends 9:00 AM)
Orals 8:00-8:15
[8:00] Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Q&As 8:15-8:20
[8:15] Q&A
Orals 8:20-8:35
[8:20] Bellman-consistent Pessimism for Offline Reinforcement Learning
Q&As 8:35-8:40
[8:35] Q&A
Orals 8:40-8:55
[8:40] A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
Q&As 8:55-9:00
[8:55] Q&A
(ends 9:00 AM)
Orals 8:00-8:15
[8:00] Partial success in closing the gap between human and machine vision
Q&As 8:15-8:20
[8:15] Q&A
Orals 8:20-8:35
[8:20] Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
Q&As 8:35-8:40
[8:35] Q&A
Orals 8:40-8:55
[8:40] Volume Rendering of Neural Implicit Surfaces
Q&As 8:55-9:00
[8:55] Q&A
(ends 9:00 AM)
8:30 a.m.
Demonstration:
(ends 9:50 AM)
9 a.m.
Town Hall:
(ends 10:00 AM)
10 a.m.
10:30 a.m.
Affinity Workshop:
(ends 1:30 PM)
11 a.m.
Affinity Workshop:
(ends 2:30 PM)
3 p.m.
Invited Talk (Posner Lecture):
Peter Bartlett
(ends 4:30 PM)
4:30 p.m.
(ends 6:00 PM)
6 p.m.
Affinity Workshop:
(ends 11:00 PM)
6:30 p.m.
11 p.m.
Invited Talk:
Alessio Figalli
(ends 12:30 AM)
THU 9 DEC
12:30 a.m.
(ends 2:00 AM)
5 a.m.
7 a.m.
Invited Talk (Interview):
Daniel Kahneman
(ends 8:30 AM)
8:30 a.m.
Demonstration:
(ends 9:35 AM)
Datasets and Benchmarks:
(ends 10:00 AM)
(ends 10:00 AM)
10 a.m.
Competition:
(ends 12:05 PM)
Roundtable:
Amanda Levendowski
(ends 11:00 AM)
11 a.m.
Datasets and Benchmarks:
(ends 2:00 PM)
3 p.m.
Invited Talk:
Meredith Broussard
(ends 4:30 PM)
4:30 p.m.
(ends 6:00 PM)
6 p.m.
Affinity Workshop:
(ends 11:00 PM)
11 p.m.
Town Hall:
(ends 12:00 AM)
FRI 10 DEC
midnight
Datasets and Benchmarks:
(ends 1:00 AM)
Orals 12:00-12:15
[12:00] Risk Monotonicity in Statistical Learning
Q&As 12:15-12:20
[12:15] Q&A
Orals 12:20-12:35
[12:20] Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
Q&As 12:35-12:40
[12:35] Q&A
Orals 12:40-12:55
[12:40] The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian
Q&As 12:55-1:00
[12:55] Q&A
(ends 1:00 AM)
Orals 12:00-12:15
[12:00] Passive attention in artificial neural networks predicts human visual selectivity
Q&As 12:15-12:20
[12:15] Q&A
Orals 12:20-12:35
[12:20] Shape As Points: A Differentiable Poisson Solver
Q&As 12:35-12:40
[12:35] Q&A
Orals 12:40-12:55
[12:40] MERLOT: Multimodal Neural Script Knowledge Models
Q&As 12:55-1:00
[12:55] Q&A
(ends 1:00 AM)
2 a.m.
Competition:
(ends 4:05 AM)
Affinity Workshop:
(ends 7:00 AM)
Affinity Workshop:
(ends 7:00 AM)
7 a.m.
Invited Talk:
Radhika Nagpal
(ends 8:30 AM)
8:30 a.m.
Demonstration:
(ends 9:35 AM)
Datasets and Benchmarks:
(ends 10:00 AM)
(ends 10:00 AM)
11 a.m.
Affinity Workshop:
(ends 3:00 PM)
noon
4 p.m.
Orals 4:00-4:15
[4:00] Moser Flow: Divergence-based Generative Modeling on Manifolds
Q&As 4:15-4:20
[4:15] Q&A
Orals 4:20-4:35
[4:20] Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Q&As 4:35-4:40
[4:35] Q&A
Orals 4:40-4:55
[4:40] Learning Treatment Effects in Panels with General Intervention Patterns
Q&As 4:55-5:00
[4:55] Q&A
(ends 5:00 PM)