96 citations · 286 across the 19 of their papers we have counts for
6 papers · 1 filter
Reliable Off-policy Evaluation for Reinforcement Learning
Jie Wang, Rui Gao, Hongyuan Zha
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from a different b…
Two-sample Test using Projected Wasserstein Distance
Jie Wang, Rui Gao, Yao Xie
We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they…
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality
Rui Gao
Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable solutions by hedging against data perturbations in Wasserstein distance. Despite its re…
Autonomous Tracking and State Estimation with Generalised Group Lasso
Rui Gao, Simo Särkkä, Rubén Claveria-Vega +1
We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The…
Distributionally Robust Weighted -Nearest Neighbors
Shixiang Zhu, Liyan Xie, Minghe Zhang +2
Learning a robust classifier from a few samples remains a key challenge in machine learning. A major thrust of research has been focused on developing -nearest neighbor (-NN)…
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes
Rui Gao, Filip Tronarp, Simo Särkkä
In this paper, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based…