sample complexity

Deep Neural Networks with Efficient Guaranteed Invariances AISTATS 2023 Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis. UAI 2023 Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories ICML 2023 Efficient Algorithms for Sparse Moment Problems without Separation COLT 2023 Efficient List-Decodable Regression using Batches ICML 2023 Efficient median of means estimator COLT 2023 Error Bounds for Learning with Vector-Valued Random Features NeurIPS 2023 Exponential Hardness of Reinforcement Learning with Linear Function Approximation COLT 2023 Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms NeurIPS 2023 Faster Adaptive Federated Learning AAAI 2023 Fast Rates for Maximum Entropy Exploration ICML 2023 Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions COLT 2023 Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond NeurIPS 2023 Feature selection and low test error in shallow low-rotation ReLU networks ICLR 2023 Find a witness or shatter: the landscape of computable PAC learning. COLT 2023 Fine-Grained Distribution-Dependent Learning Curves COLT 2023 Finite-Time Analysis of Single-Timescale Actor-Critic NeurIPS 2023 From Robustness to Privacy and Back ICML 2023 Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator AAAI 2023 Gradient-Based Feature Learning under Structured Data NeurIPS 2023 Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds ICML 2023 How many samples are needed to leverage smoothness? NeurIPS 2023 Hypothesis Selection with Memory Constraints NeurIPS 2023 Improper Multiclass Boosting COLT 2023 Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning AISTATS 2023