asymptotic variance

Accelerating Constrained Sampling: A Large Deviations Approach JMLR 2026 Cost-Aware Optimized Front-Door Experimental Design CLeaR 2026 Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics ICML 2025 Learning Parametric Distributions from Samples and Preferences ICML 2025 Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations ICLR 2025 Near-Optimality of Contrastive Divergence Algorithms NeurIPS 2024 Policy Evaluation for Variance in Average Reward Reinforcement Learning ICML 2024 The surprising efficiency of temporal difference learning for rare event prediction NeurIPS 2024 Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains ICML 2023 The Implicit Delta Method NeurIPS 2022 The optimal noise in noise-contrastive learning is not what you think UAI 2022 Asymptotically Best Causal Effect Identification with Multi-Armed Bandits NeurIPS 2021 Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data NeurIPS 2021 A Unified Statistically Efficient Estimation Framework for Unnormalized Models AISTATS 2020 Cross-validation Confidence Intervals for Test Error NeurIPS 2020 Fisher Efficient Inference of Intractable Models NeurIPS 2019 Rates of estimation for determinantal point processes COLT 2017 On Estimating Causal Effects based on Supplemental Variables AISTATS 2014 Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices NeurIPS 2010 Stochastic Composite Likelihood JMLR 2010 A Unified Near-Optimal Estimator For Dimension Reduction in l_α (0<α≤ 2) Using Stable Random Projections NeurIPS 2007