Poster "variance reduction" Papers
13 papers found
Conference
Better Estimation of the Kullback--Leibler Divergence Between Language Models
Afra Amini, Tim Vieira, Ryan Cotterell
NEURIPS 2025arXiv:2504.10637
4
citations
Doubly Optimal Policy Evaluation for Reinforcement Learning
Shuze Liu, Claire Chen, Shangtong Zhang
ICLR 2025arXiv:2410.02226
5
citations
Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning
Claire Chen, Shuze Liu, Shangtong Zhang
ICLR 2025arXiv:2410.05655
1
citations
Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models
Theo Bourdais, Houman Owhadi
ICLR 2025arXiv:2409.17267
2
citations
On the Convergence of Stochastic Smoothed Multi-Level Compositional Gradient Descent Ascent
Xinwen Zhang, Hongchang Gao
NEURIPS 2025
PseuZO: Pseudo-Zeroth-Order Algorithm for Training Deep Neural Networks
Pengyun Yue, Xuanlin Yang, Mingqing Xiao et al.
NEURIPS 2025
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams et al.
ICLR 2025arXiv:2410.02490
1
citations
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
Dingzhi Yu, Yunuo Cai, Wei Jiang et al.
ICML 2024arXiv:2403.03562
7
citations
Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
Shuze Liu, Shangtong Zhang
ICML 2024arXiv:2301.13734
7
citations
Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
Undral Byambadalai, Tatsushi Oka, Shota Yasui
ICML 2024arXiv:2407.16037
6
citations
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Yu-Xiang Wang et al.
ICML 2024arXiv:2308.08858
2
citations
Non-convex Stochastic Composite Optimization with Polyak Momentum
Yuan Gao, Anton Rodomanov, Sebastian Stich
ICML 2024arXiv:2403.02967
13
citations
SILVER: Single-loop variance reduction and application to federated learning
Kazusato Oko, Shunta Akiyama, Denny Wu et al.
ICML 2024