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Learning Theory

5,334 papers

Papers per year

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Papers

Why Random Pruning Is All We Need to Start Sparse ICML 2023
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks ICML 2023
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization ICML 2023
Contextual Reliability: When Different Features Matter in Different Contexts ICML 2023
Multicalibration as Boosting for Regression ICML 2023
Learning Distributions over Quantum Measurement Outcomes ICML 2023
Hidden Symmetries of ReLU Networks ICML 2023
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances ICML 2023
System Identification of Neural Systems: If We Got It Right, Would We Know? ICML 2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters ICML 2023
Algorithmic Collective Action in Machine Learning ICML 2023
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023
AdaBoost is not an Optimal Weak to Strong Learner ICML 2023
Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach ICML 2023
The Power of Uniform Sampling for k-Median ICML 2023
Achieving Linear Speedup in Non-IID Federated Bilevel Learning ICML 2023
The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics ICML 2023
Combinatorial Neural Bandits ICML 2023
Maximal Initial Learning Rates in Deep ReLU Networks ICML 2023
Unconstrained Online Learning with Unbounded Losses ICML 2023
Smooth Non-stationary Bandits ICML 2023
Statistical Indistinguishability of Learning Algorithms ICML 2023
How Does Information Bottleneck Help Deep Learning? ICML 2023
Learnability and Algorithm for Continual Learning ICML 2023
Online Learning with Feedback Graphs: The True Shape of Regret ICML 2023