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Theory

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Papers

Collapsing Bandits and Their Application to Public Health Intervention NIPS 2020
On the linearity of large non-linear models: when and why the tangent kernel is constant NIPS 2020
On the Equivalence between Online and Private Learnability beyond Binary Classification NIPS 2020
Probably Approximately Correct Constrained Learning NIPS 2020
Reservoir Computing meets Recurrent Kernels and Structured Transforms NIPS 2020
PAC-Bayes Analysis Beyond the Usual Bounds NIPS 2020
Compositional Explanations of Neurons NIPS 2020
Understanding Global Feature Contributions With Additive Importance Measures NIPS 2020
Neural Execution Engines: Learning to Execute Subroutines NIPS 2020
Online Multitask Learning with Long-Term Memory NIPS 2020
On Reward-Free Reinforcement Learning with Linear Function Approximation NIPS 2020
Robust large-margin learning in hyperbolic space NIPS 2020
PAC-Bayesian Bound for the Conditional Value at Risk NIPS 2020
Batch normalization provably avoids ranks collapse for randomly initialised deep networks NIPS 2020
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices NIPS 2020
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise NIPS 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification NIPS 2020
Weak Form Generalized Hamiltonian Learning NIPS 2020
Almost Surely Stable Deep Dynamics NIPS 2020
Linear-Sample Learning of Low-Rank Distributions NIPS 2020
Self-Adaptive Training: beyond Empirical Risk Minimization NIPS 2020
A new convergent variant of Q-learning with linear function approximation NIPS 2020
Neural Networks with Small Weights and Depth-Separation Barriers NIPS 2020
Asymptotically Optimal Exact Minibatch Metropolis-Hastings NIPS 2020
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory NIPS 2020