conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5,312 papers

Papers per year

Papers

Capacity Bounded Differential Privacy NIPS 2019
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates NIPS 2019
Uniform convergence may be unable to explain generalization in deep learning NIPS 2019
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals NIPS 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent NIPS 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation NIPS 2019
On the Convergence Rate of Training Recurrent Neural Networks NIPS 2019
Minimizers of the Empirical Risk and Risk Monotonicity NIPS 2019
On Robustness to Adversarial Examples and Polynomial Optimization NIPS 2019
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs NIPS 2019
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees NIPS 2019
Statistical-Computational Tradeoff in Single Index Models NIPS 2019
Dying Experts: Efficient Algorithms with Optimal Regret Bounds NIPS 2019
Learning-Based Low-Rank Approximations NIPS 2019
Theoretical Analysis of Adversarial Learning: A Minimax Approach NIPS 2019
Generalization Error Analysis of Quantized Compressive Learning NIPS 2019
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks NIPS 2019
Sample Complexity of Learning Mixture of Sparse Linear Regressions NIPS 2019
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective NIPS 2019
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis NIPS 2019
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems NIPS 2019
Hypothesis Set Stability and Generalization NIPS 2019
Learning to Screen NIPS 2019
The Implicit Bias of AdaGrad on Separable Data NIPS 2019
Certified Adversarial Robustness with Additive Noise NIPS 2019