96 citations · 286 across the 19 of their papers we have counts for
19 papers
A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization
Luhao Zhang, Jincheng Yang, Rui Gao
We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probab…
Absolute Zero-Shot Learning
Rui Gao, Fan Wan, Daniel Organisciak +6
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i.e., training a classifier with zero r…
Sinkhorn Distributionally Robust Optimization
Jie Wang, Rui Gao, Yao Xie
We study distributionally robust optimization with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We derive a convex programming dual refo…
Robust Hypothesis Testing with Wasserstein Uncertainty Sets
Liyan Xie, Rui Gao, Yao Xie
We consider a data-driven robust hypothesis test where the optimal test will minimize the worst-case performance regarding distributions that are close to the empirical distributio…
Hierarchical Non-Stationary Temporal Gaussian Processes With -Regularization
Zheng Zhao, Rui Gao, Simo Särkkä
This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e.g., length-scale) are modeled as G…
Two-sample Test with Kernel Projected Wasserstein Distance
Jie Wang, Rui Gao, Yao Xie
We develop a kernel projected Wasserstein distance for the two-sample test, an essential building block in statistics and machine learning: given two sets of samples, to determine…
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Hao Wang, Rui Gao, Flavio P. Calmon
Machine learning models trained by different optimization algorithms under different data distributions can exhibit distinct generalization behaviors. In this paper, we analyze the…
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…
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators
Qitian Wu, Rui Gao, Hongyuan Zha
There are two types of deep generative models: explicit and implicit. The former defines an explicit density form that allows likelihood inference; while the latter targets a flexi…
Iterated Extended Kalman Smoother-based Variable Splitting for -Regularized State Estimation
Rui Gao, Filip Tronarp, Simo Särkkä
In this paper, we propose a new framework for solving state estimation problems with an additional sparsity-promoting -regularizer term. We first formulate such problems as mi…
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
Rui Gao, Liyan Xie, Yao Xie +1
We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null…
Wasserstein Distributionally Robust Optimization and Variation Regularization
Rui Gao, Xi Chen, Anton J. Kleywegt
Wasserstein distributionally robust optimization (DRO) has recently achieved empirical success for various applications in operations research and machine learning, owing partly to…
Distributionally Robust Stochastic Optimization with Dependence Structure
Rui Gao, Anton J. Kleywegt
Distributionally robust stochastic optimization (DRSO) is a framework for decision-making problems under certainty, which finds solutions that perform well for a chosen set of prob…
Distributionally Robust Stochastic Optimization with Wasserstein Distance
Rui Gao, Anton J. Kleywegt
Distributionally robust stochastic optimization (DRSO) is an approach to optimization under uncertainty in which, instead of assuming that there is a known true underlying probabil…