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Curvature Regularization to Prevent Distortion in Graph Embedding
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Mon Dec 07 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Representation/Relational
On the Equivalence between Online and Private Learnability beyond Binary Classification
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Mon Dec 07 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
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Mon Dec 07 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Language/Audio Applications
Unsupervised Sound Separation Using Mixture Invariant Training
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Mon Dec 07 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Reinforcement Learning
First Order Constrained Optimization in Policy Space
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Mon Dec 07 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Representation/Relational
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
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Mon Dec 07 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
Using noise to probe recurrent neural network structure and prune synapses
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Mon Dec 07 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Language/Audio Applications
Investigating Gender Bias in Language Models Using Causal Mediation Analysis
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Mon Dec 07 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Reinforcement Learning
CoinDICE: Off-Policy Confidence Interval Estimation
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Mon Dec 07 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Representation/Relational
Joint Contrastive Learning with Infinite Possibilities
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Mon Dec 07 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
Interpretable Sequence Learning for Covid-19 Forecasting
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Mon Dec 07 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Language/Audio Applications
A Simple Language Model for Task-Oriented Dialogue
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Mon Dec 07 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Reinforcement Learning
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
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Mon Dec 07 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Representation/Relational
Neural Methods for Point-wise Dependency Estimation
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Mon Dec 07 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
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Mon Dec 07 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Language/Audio Applications
ConvBERT: Improving BERT with Span-based Dynamic Convolution
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Mon Dec 07 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
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Mon Dec 07 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Representation/Relational
Design Space for Graph Neural Networks
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Mon Dec 07 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
Demixed shared component analysis of neural population data from multiple brain areas
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Mon Dec 07 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Language/Audio Applications
Cross-lingual Retrieval for Iterative Self-Supervised Training
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Mon Dec 07 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
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Mon Dec 07 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Representation/Relational
Debiased Contrastive Learning
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Mon Dec 07 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
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Mon Dec 07 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Language/Audio Applications
DynaBERT: Dynamic BERT with Adaptive Width and Depth
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Mon Dec 07 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Bayesian Multi-type Mean Field Multi-agent Imitation Learning
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Mon Dec 07 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Representation/Relational
The Autoencoding Variational Autoencoder
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Mon Dec 07 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: COVID/Health/Bio Applications
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
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Mon Dec 07 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Language/Audio Applications
Incorporating Pragmatic Reasoning Communication into Emergent Language
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Mon Dec 07 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
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Mon Dec 07 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Representation/Relational
Unsupervised Representation Learning by Invariance Propagation
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Mon Dec 07 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Language/Audio Applications
De-Anonymizing Text by Fingerprinting Language Generation
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Mon Dec 07 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Safe Reinforcement Learning via Curriculum Induction
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Clustering/Ranking
Simple and Scalable Sparse k-means Clustering via Feature Ranking
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Robust Density Estimation under Besov IPM Losses
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Vision Applications
DISK: Learning local features with policy gradient
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Deep Learning
MeshSDF: Differentiable Iso-Surface Extraction
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Interferobot: aligning an optical interferometer by a reinforcement learning agent
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Social/Privacy
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
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Tue Dec 08 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Learning Theory
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Clustering/Ranking
Simultaneous Preference and Metric Learning from Paired Comparisons
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Almost Surely Stable Deep Dynamics
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Vision Applications
Wasserstein Distances for Stereo Disparity Estimation
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Deep Learning
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Reinforcement Learning
On Efficiency in Hierarchical Reinforcement Learning
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Social/Privacy
Private Identity Testing for High-Dimensional Distributions
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Tue Dec 08 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Learning Theory
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Clustering/Ranking
Learning Optimal Representations with the Decodable Information Bottleneck
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Vision Applications
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Deep Learning
Monotone operator equilibrium networks
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Finite-Time Analysis for Double Q-learning
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Social/Privacy
Permute-and-Flip: A new mechanism for differentially private selection
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Tue Dec 08 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Learning Theory
A Bandit Learning Algorithm and Applications to Auction Design
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Clustering/Ranking
Manifold structure in graph embeddings
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
A Theoretical Framework for Target Propagation
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Vision Applications
Learning Semantic-aware Normalization for Generative Adversarial Networks
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Deep Learning
What Do Neural Networks Learn When Trained With Random Labels?
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Social/Privacy
Smoothed Analysis of Online and Differentially Private Learning
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Tue Dec 08 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Learning Theory
An Optimal Elimination Algorithm for Learning a Best Arm
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Clustering/Ranking
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Vision Applications
Neural Sparse Voxel Fields
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Deep Learning
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Social/Privacy
Optimal Private Median Estimation under Minimal Distributional Assumptions
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Tue Dec 08 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Learning Theory
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Clustering/Ranking
Classification with Valid and Adaptive Coverage
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Information theoretic limits of learning a sparse rule
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Vision Applications
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Deep Learning
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Model-based Policy Optimization with Unsupervised Model Adaptation
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Social/Privacy
Assisted Learning: A Framework for Multi-Organization Learning
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Tue Dec 08 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Learning Theory
PAC-Bayesian Bound for the Conditional Value at Risk
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Clustering/Ranking
On ranking via sorting by estimated expected utility
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Vision Applications
Learning to Detect Objects with a 1 Megapixel Event Camera
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Deep Learning
The phase diagram of approximation rates for deep neural networks
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Social/Privacy
Higher-Order Certification For Randomized Smoothing
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Tue Dec 08 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Learning Theory
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
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Tue Dec 08 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Dynamical Sys/Density/Sparsity
Logarithmic Pruning is All You Need
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Tue Dec 08 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Vision Applications
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
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Tue Dec 08 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Deep Learning
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
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Tue Dec 08 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
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Tue Dec 08 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Learning Theory
Hedging in games: Faster convergence of external and swap regrets
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Tue Dec 08 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: Learning Theory
Online Bayesian Persuasion
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Tue Dec 08 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Vision Applications
Self-Supervised Visual Representation Learning from Hierarchical Grouping
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Tue Dec 08 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
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Tue Dec 08 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Reinforcement Learning with Augmented Data
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Tue Dec 08 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Vision Applications
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
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Tue Dec 08 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
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Tue Dec 08 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Sub-sampling for Efficient Non-Parametric Bandit Exploration
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Tue Dec 08 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Vision Applications
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
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Tue Dec 08 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
Compositional Visual Generation with Energy Based Models
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Tue Dec 08 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
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Tue Dec 08 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Vision Applications
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
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Tue Dec 08 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
Certified Monotonic Neural Networks
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Tue Dec 08 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Reinforcement Learning
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
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Tue Dec 08 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Vision Applications
Measuring Robustness to Natural Distribution Shifts in Image Classification
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Tue Dec 08 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
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Tue Dec 08 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Policy Improvement via Imitation of Multiple Oracles
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Tue Dec 08 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Vision Applications
Curriculum By Smoothing
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Tue Dec 08 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
On Correctness of Automatic Differentiation for Non-Differentiable Functions
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Tue Dec 08 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
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Tue Dec 08 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Vision Applications
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
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Tue Dec 08 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
The Complete Lasso Tradeoff Diagram
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Tue Dec 08 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Avoiding Side Effects in Complex Environments
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Tue Dec 08 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Deep Learning/Theory
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
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Tue Dec 08 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Reinforcement Learning
Preference-based Reinforcement Learning with Finite-Time Guarantees
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
Compositional Generalization by Learning Analytical Expressions
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Meta-trained agents implement Bayes-optimal agents
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
A mathematical model for automatic differentiation in machine learning
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Deep Learning
What if Neural Networks had SVDs?
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
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Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Optimization
Adam with Bandit Sampling for Deep Learning
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
Modern Hopfield Networks and Attention for Immune Repertoire Classification
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Linear Dynamical Systems as a Core Computational Primitive
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
A kernel test for quasi-independence
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Deep Learning
Practical Quasi-Newton Methods for Training Deep Neural Networks
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
A Randomized Algorithm to Reduce the Support of Discrete Measures
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Differentially-Private Federated Linear Bandits
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Wed Dec 09 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Optimization
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
Fourier Sparse Leverage Scores and Approximate Kernel Learning
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Deep Learning
Triple descent and the two kinds of overfitting: where & why do they appear?
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
A/B Testing in Dense Large-Scale Networks: Design and Inference
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
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Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Optimization
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
A causal view of compositional zero-shot recognition
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Uncertainty-aware Self-training for Few-shot Text Classification
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
BOSS: Bayesian Optimization over String Spaces
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Deep Learning
On the linearity of large non-linear models: when and why the tangent kernel is constant
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Prediction with Corrupted Expert Advice
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Wed Dec 09 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Optimization
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
HiPPO: Recurrent Memory with Optimal Polynomial Projections
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
Fast geometric learning with symbolic matrices
[ Paper ]
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Deep Learning
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
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Wed Dec 09 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Optimization
Minibatch Stochastic Approximate Proximal Point Methods
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
Barking up the right tree: an approach to search over molecule synthesis DAGs
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
Training Stronger Baselines for Learning to Optimize
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Deep Learning
Proximal Mapping for Deep Regularization
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Towards Safe Policy Improvement for Non-Stationary MDPs
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Wed Dec 09 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Optimization
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
[ Paper ]
Spotlight
Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
[ Paper ]
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Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
[ Paper ]
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Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
Learning Linear Programs from Optimal Decisions
[ Paper ]
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Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Deep Learning
BoxE: A Box Embedding Model for Knowledge Base Completion
[ Paper ]
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Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
Differentiable Causal Discovery from Interventional Data
[ Paper ]
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Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
[ Paper ]
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Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Optimization
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
[ Paper ]
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Wed Dec 09 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
Experimental design for MRI by greedy policy search
[ Paper ]
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Wed Dec 09 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
[ Paper ]
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Wed Dec 09 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Kernel Methods/Optimization
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
[ Paper ]
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Wed Dec 09 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
[ Paper ]
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Wed Dec 09 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
[ Paper ]
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Wed Dec 09 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Optimization
Linearly Converging Error Compensated SGD
[ Paper ]
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Wed Dec 09 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: Optimization
Learning Augmented Energy Minimization via Speed Scaling
[ Paper ]
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Wed Dec 09 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: COVID/Applications/Composition
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
[ Paper ]
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Wed Dec 09 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
[ Paper ]
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Wed Dec 09 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
Efficient semidefinite-programming-based inference for binary and multi-class MRFs
[ Paper ]
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Wed Dec 09 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
[ Paper ]
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Wed Dec 09 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Vision Applications
Distribution Matching for Crowd Counting
[ Paper ]
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Wed Dec 09 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
Improved Schemes for Episodic Memory-based Lifelong Learning
[ Paper ]
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Wed Dec 09 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Learning Theory
On Adaptive Distance Estimation
[ Paper ]
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Wed Dec 09 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Vision Applications
Texture Interpolation for Probing Visual Perception
[ Paper ]
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Wed Dec 09 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
[ Paper ]
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Wed Dec 09 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Learning Theory
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
[ Paper ]
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Wed Dec 09 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Vision Applications
Consistent Structural Relation Learning for Zero-Shot Segmentation
[ Paper ]
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Wed Dec 09 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
Uncertainty Aware Semi-Supervised Learning on Graph Data
[ Paper ]
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Wed Dec 09 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Learning Theory
Delay and Cooperation in Nonstochastic Linear Bandits
[ Paper ]
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Wed Dec 09 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Vision Applications
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
[ Paper ]
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Wed Dec 09 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
Rethinking Importance Weighting for Deep Learning under Distribution Shift
[ Paper ]
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Wed Dec 09 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Learning Theory
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
[ Paper ]
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Wed Dec 09 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Vision Applications
ShapeFlow: Learnable Deformation Flows Among 3D Shapes
[ Paper ]
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Wed Dec 09 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
Modular Meta-Learning with Shrinkage
[ Paper ]
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Wed Dec 09 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Learning Theory
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
[ Paper ]
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Wed Dec 09 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Vision Applications
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
[ Paper ]
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Wed Dec 09 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
JAX MD: A Framework for Differentiable Physics
[ Paper ]
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Wed Dec 09 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Learning Theory
A Tight Lower Bound and Efficient Reduction for Swap Regret
[ Paper ]
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Wed Dec 09 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Vision Applications
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
[ Paper ]
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Wed Dec 09 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
[ Paper ]
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Wed Dec 09 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Learning Theory
Estimation of Skill Distribution from a Tournament
[ Paper ]
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Wed Dec 09 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Vision Applications
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
[ Paper ]
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Wed Dec 09 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Learning Theory
Optimal Prediction of the Number of Unseen Species with Multiplicity
[ Paper ]
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Wed Dec 09 08:30 PM -- 08:40 PM (PST) @ Orals & Spotlights: Learning Theory
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Sampling from a k-DPP without looking at all items
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Learning Graph Structure With A Finite-State Automaton Layer
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Self-Supervised Relational Reasoning for Representation Learning
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Deep Learning
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Neuroscience
Stable and expressive recurrent vision models
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Optimization/Theory
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
[ Paper ]
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Thu Dec 10 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Bandit Linear Control
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Non-parametric Models for Non-negative Functions
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Pointer Graph Networks
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Object-Centric Learning with Slot Attention
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Deep Learning
On Power Laws in Deep Ensembles
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Neuroscience
Identifying Learning Rules From Neural Network Observables
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Optimization/Theory
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
[ Paper ]
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Thu Dec 10 07:10 AM -- 07:20 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Neural Dynamic Policies for End-to-End Sensorimotor Learning
[ Paper ]
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Thu Dec 10 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Distribution-free binary classification: prediction sets, confidence intervals and calibration
[ Paper ]
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Thu Dec 10 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Telescoping Density-Ratio Estimation
[ Paper ]
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Thu Dec 10 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Deep Learning
Learning the Geometry of Wave-Based Imaging
[ Paper ]
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Thu Dec 10 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Neuroscience
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
[ Paper ]
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Thu Dec 10 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Optimization/Theory
Conformal Symplectic and Relativistic Optimization
[ Paper ]
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Thu Dec 10 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Effective Diversity in Population Based Reinforcement Learning
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Factor Graph Grammars
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Deep Learning
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Neuroscience
Modeling Shared responses in Neuroimaging Studies through MultiView ICA
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Optimization/Theory
Random Reshuffling is Not Always Better
[ Paper ]
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Thu Dec 10 07:30 AM -- 07:40 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Deep Learning
Sparse and Continuous Attention Mechanisms
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Neuroscience
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Optimization/Theory
The Statistical Complexity of Early-Stopped Mirror Descent
[ Paper ]
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Thu Dec 10 07:50 AM -- 08:00 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Reward Propagation Using Graph Convolutional Networks
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Stochastic Normalizing Flows
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Deep Learning
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Neuroscience
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Optimization/Theory
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
[ Paper ]
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Thu Dec 10 08:00 AM -- 08:10 AM (PST) @ Orals & Spotlights: Reinforcement Learning
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Confidence sequences for sampling without replacement
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
Generative Neurosymbolic Machines
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Deep Learning
Directional convergence and alignment in deep learning
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Neuroscience
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Optimization/Theory
Towards Problem-dependent Optimal Learning Rates
[ Paper ]
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Thu Dec 10 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Latent World Models For Intrinsically Motivated Exploration
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Statistical and Topological Properties of Sliced Probability Divergences
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Graph/Relational/Theory
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Unsupervised/Probabilistic
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Deep Learning
Neural Controlled Differential Equations for Irregular Time Series
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Neuroscience
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Optimization/Theory
On Uniform Convergence and Low-Norm Interpolation Learning
[ Paper ]
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Thu Dec 10 08:20 AM -- 08:30 AM (PST) @ Orals & Spotlights: Reinforcement Learning
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
[ Paper ]
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Thu Dec 10 08:30 AM -- 08:40 AM (PST) @ Orals & Spotlights: Probabilistic Models/Statistics
Testing Determinantal Point Processes
[ Paper ]
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Thu Dec 10 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Optimization
Submodular Maximization Through Barrier Functions
[ Paper ]
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Thu Dec 10 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
A Study on Encodings for Neural Architecture Search
[ Paper ]
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Thu Dec 10 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Deep Learning
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
[ Paper ]
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Thu Dec 10 07:00 PM -- 07:10 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds
[ Paper ]
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Thu Dec 10 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Optimization
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
[ Paper ]
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Thu Dec 10 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
[ Paper ]
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Thu Dec 10 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Deep Learning
Kernel Based Progressive Distillation for Adder Neural Networks
[ Paper ]
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Thu Dec 10 07:10 PM -- 07:20 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
Sinkhorn Natural Gradient for Generative Models
[ Paper ]
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Thu Dec 10 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Optimization
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
[ Paper ]
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Thu Dec 10 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
Evolving Normalization-Activation Layers
[ Paper ]
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Thu Dec 10 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Deep Learning
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
[ Paper ]
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Thu Dec 10 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
NVAE: A Deep Hierarchical Variational Autoencoder
[ Paper ]
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Thu Dec 10 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Optimization
How many samples is a good initial point worth in Low-rank Matrix Recovery?
[ Paper ]
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Thu Dec 10 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
Open Graph Benchmark: Datasets for Machine Learning on Graphs
[ Paper ]
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Thu Dec 10 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Deep Learning
Collegial Ensembles
[ Paper ]
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Thu Dec 10 07:30 PM -- 07:40 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
Reciprocal Adversarial Learning via Characteristic Functions
[ Paper ]
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Thu Dec 10 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Optimization
Projection Robust Wasserstein Distance and Riemannian Optimization
[ Paper ]
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Thu Dec 10 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
[ Paper ]
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Thu Dec 10 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Deep Learning
Finite Versus Infinite Neural Networks: an Empirical Study
[ Paper ]
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Thu Dec 10 07:50 PM -- 08:00 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
[ Paper ]
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Thu Dec 10 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Optimization
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
[ Paper ]
Spotlight
Thu Dec 10 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
MCUNet: Tiny Deep Learning on IoT Devices
[ Paper ]
Spotlight
Thu Dec 10 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Deep Learning
Estimating Training Data Influence by Tracing Gradient Descent
[ Paper ]
Spotlight
Thu Dec 10 08:00 PM -- 08:10 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
Baxter Permutation Process
[ Paper ]
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Thu Dec 10 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Optimization
SGD with shuffling: optimal rates without component convexity and large epoch requirements
[ Paper ]
Spotlight
Thu Dec 10 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Health/AutoML/(Soft|Hard)ware
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
[ Paper ]
Spotlight
Thu Dec 10 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Deep Learning
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
[ Paper ]
Spotlight
Thu Dec 10 08:10 PM -- 08:20 PM (PST) @ Orals & Spotlights: Neuroscience/Probabilistic
Flexible mean field variational inference using mixtures of non-overlapping exponential families
[ Paper ]
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Thu Dec 10 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Optimization
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
[ Paper ]
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Thu Dec 10 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Deep Learning
Part-dependent Label Noise: Towards Instance-dependent Label Noise
[ Paper ]