variance estimation

Variance Estimation and Selecting Good Estimands for Causal Effect Queries UAI 2026 General Uncertainty Estimation with Delta Variances AAAI 2025 Variance-Aware Estimation of Kernel Mean Embedding JMLR 2025 Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning NeurIPS 2024 Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions AAAI 2024 One Step Closer to Unbiased Aleatoric Uncertainty Estimation AAAI 2024 Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning ICLR 2024 Robust variance-regularized risk minimization with concomitant scaling AISTATS 2024 Statistical Uncertainty in Word Embeddings: GloVe-V EMNLP 2024 Variance estimation in compound decision theory under boundedness NeurIPS 2024 Variance estimation in graphs with the fused lasso JMLR 2024 CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments NeurIPS 2023 Faithful Heteroscedastic Regression with Neural Networks AISTATS 2023 High Precision Causal Model Evaluation with Conditional Randomization NeurIPS 2023 Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion NeurIPS 2023 STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition ICML 2023 Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models ICLR 2022 Finite Sample Analysis Of Dynamic Regression Parameter Learning NeurIPS 2022 On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks ICLR 2022 Bootstrapping Fitted Q-Evaluation for Off-Policy Inference ICML 2021 High-Confidence Off-Policy (or Counterfactual) Variance Estimation AAAI 2021 Learning the Truth From Only One Side of the Story AISTATS 2021 Moment Multicalibration for Uncertainty Estimation COLT 2021 Uncertainty-Driven Loss for Single Image Super-Resolution NeurIPS 2021 V-statistics and Variance Estimation JMLR 2021