6 citations · 7 across the 3 of their papers we have counts for
6 papers
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…
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…
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…
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…