Learning Theory

A Computable Definition of the Spectral Bias AAAI 2022 ActiveHedge: Hedge meets Active Learning ICML 2022 Active Learning Polynomial Threshold Functions NeurIPS 2022 Active Learning with Neural Networks: Insights from Nonparametric Statistics NeurIPS 2022 Active Ranking without Strong Stochastic Transitivity NeurIPS 2022 Actor-critic is implicitly biased towards high entropy optimal policies ICLR 2022 Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits ICML 2022 Adaptive Data Analysis with Correlated Observations ICML 2022 Adaptive Oracle-Efficient Online Learning NeurIPS 2022 Adaptive Sampling for Discovery NeurIPS 2022 Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization NeurIPS 2022 Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization ICML 2022 Adversarial Robustness is at Odds with Lazy Training NeurIPS 2022 A Fast Algorithm for PAC Combinatorial Pure Exploration AAAI 2022 A Few More Examples May Be Worth Billions of Parameters EMNLP 2022 A Fourier Approach to Mixture Learning NeurIPS 2022 A general approximation lower bound in L^p norm, with applications to feed-forward neural networks NeurIPS 2022 A general sample complexity analysis of vanilla policy gradient AISTATS 2022 Agnostic Learnability of Halfspaces via Logistic Loss ICML 2022 A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs ICLR 2022 A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits AISTATS 2022 Algorithms and Hardness for Learning Linear Thresholds from Label Proportions NeurIPS 2022 Algorithms for learning a mixture of linear classifiers ALT 2022 Algorithms with Prediction Portfolios NeurIPS 2022 Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions AISTATS 2022