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Learning Theory

5,312 papers

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Papers

Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms UAI 2021
Simple combinatorial algorithms for combinatorial bandits: corruptions and approximations UAI 2021
Minimax sample complexity for turn-based stochastic game UAI 2021
Generalization error bounds for deep unfolding RNNs UAI 2021
Graph-based semi-supervised learning through the lens of safety UAI 2021
Strategically efficient exploration in competitive multi-agent reinforcement learning UAI 2021
Learning in Multi-Player Stochastic Games UAI 2021
Learning and certification under instance-targeted poisoning UAI 2021
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds NIPS 2020
Quantitative Propagation of Chaos for SGD in Wide Neural Networks NIPS 2020
A Closer Look at the Training Strategy for Modern Meta-Learning NIPS 2020
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law NIPS 2020
Hardness of Learning Neural Networks with Natural Weights NIPS 2020
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems NIPS 2020
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity NIPS 2020
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity NIPS 2020
The Implications of Local Correlation on Learning Some Deep Functions NIPS 2020
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix NIPS 2020
CASTLE: Regularization via Auxiliary Causal Graph Discovery NIPS 2020
On Numerosity of Deep Neural Networks NIPS 2020
Margins are Insufficient for Explaining Gradient Boosting NIPS 2020
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks NIPS 2020
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits NIPS 2020
Statistical-Query Lower Bounds via Functional Gradients NIPS 2020
Towards Problem-dependent Optimal Learning Rates NIPS 2020