finite-sample guarantee

Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions CLeaR 2026 Quantifying and Understanding Uncertainty in Large Reasoning Models ACL 2026 Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction ICML 2025 Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances ICML 2025 Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms ICML 2024 Bagging Provides Assumption-free Stability JMLR 2024 Conformalized Adaptive Forecasting of Heterogeneous Trajectories ICML 2024 Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint ICML 2024 Optimal estimation of Gaussian (poly)trees AISTATS 2024 Adaptive Clustering Using Kernel Density Estimators JMLR 2023 Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? L4DC 2023 Distribution-Free Statistical Dispersion Control for Societal Applications NeurIPS 2023 FaiREE: fair classification with finite-sample and distribution-free guarantee ICLR 2023 Finite-sample guarantees for Nash Q-learning with linear function approximation UAI 2023 Finite-Sample Symmetric Mean Estimation with Fisher Information Rate COLT 2023 Conformal Off-Policy Prediction in Contextual Bandits NeurIPS 2022 Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees L4DC 2022 Optimal Binary Classification Beyond Accuracy NeurIPS 2022 Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability AAAI 2022 Sparsity in Partially Controllable Linear Systems ICML 2022 Conformal Bayesian Computation NeurIPS 2021 Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms NeurIPS 2021 A Robust Functional EM Algorithm for Incomplete Panel Count Data NeurIPS 2020 Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms JMLR 2020 Information-Theoretic Considerations in Batch Reinforcement Learning ICML 2019