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Learning Theory

5,312 papers

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Papers

Globally optimal score-based learning of directed acyclic graphs in high-dimensions NIPS 2019
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin NIPS 2019
Distribution-Independent PAC Learning of Halfspaces with Massart Noise NIPS 2019
PAC-Bayes under potentially heavy tails NIPS 2019
Private Stochastic Convex Optimization with Optimal Rates NIPS 2019
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle NIPS 2019
PAC-Bayes Un-Expected Bernstein Inequality NIPS 2019
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback NIPS 2019
Model Selection for Contextual Bandits NIPS 2019
Worst-Case Regret Bounds for Exploration via Randomized Value Functions NIPS 2019
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets NIPS 2019
Model Similarity Mitigates Test Set Overuse NIPS 2019
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks NIPS 2019
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models NIPS 2019
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration NIPS 2019
On the Power and Limitations of Random Features for Understanding Neural Networks NIPS 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? NIPS 2019
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies NIPS 2019
Combinatorial Bandits with Relative Feedback NIPS 2019
Is Deeper Better only when Shallow is Good? NIPS 2019
Pure Exploration with Multiple Correct Answers NIPS 2019
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers NIPS 2019
Online Forecasting of Total-Variation-bounded Sequences NIPS 2019
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds NIPS 2019
Are sample means in multi-armed bandits positively or negatively biased? NIPS 2019