Learning Theory

Active representation learning for general task space with applications in robotics NeurIPS 2023 Activity Grammars for Temporal Action Segmentation NeurIPS 2023 AdaBoost is not an Optimal Weak to Strong Learner ICML 2023 Adaptation to Misspecified Kernel Regularity in Kernelised Bandits AISTATS 2023 Adapting to game trees in zero-sum imperfect information games ICML 2023 Adaptive Data Analysis in a Balanced Adversarial Model NeurIPS 2023 Adaptive Regret for Control of Time-Varying Dynamics L4DC 2023 Adaptive Sample Selection for Robust Learning Under Label Noise WACV 2023 Adaptive Selective Sampling for Online Prediction with Experts NeurIPS 2023 Adjective Scale Probe: Can Language Models Encode Formal Semantics Information? AAAI 2023 Adversarial Attacks on Adversarial Bandits ICLR 2023 Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness NeurIPS 2023 Adversarially Robust Learning with Tolerance ALT 2023 Adversarially Robust Learning with Uncertain Perturbation Sets NeurIPS 2023 Adversarially Robust PAC Learnability of Real-Valued Functions ICML 2023 Adversarial Resilience in Sequential Prediction via Abstention NeurIPS 2023 A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel ICML 2023 A Finite Sample Complexity Bound for Distributionally Robust Q-learning AISTATS 2023 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning ICLR 2023 A General Regret Bound of Preconditioned Gradient Method for DNN Training CVPR 2023 A General Representation Learning Framework with Generalization Performance Guarantees ICML 2023 Agnostically Learning Single-Index Models using Omnipredictors NeurIPS 2023 Agnostic Learning of General ReLU Activation Using Gradient Descent ICLR 2023 Agnostic PAC Learning of k-juntas Using L_2-Polynomial Regression AISTATS 2023 A law of adversarial risk, interpolation, and label noise ICLR 2023