no code implementations • 18 Apr 2024 • Ting-Ruen Wei, Michele Hell, Dang Bich Thuy Le, Aren Vierra, Ran Pang, Mahesh Patel, Young Kang, Yuling Yan
This model is then iteratively refined for the domain adaptation task, generating pseudo-masks for our private, unannotated breast US dataset.
no code implementations • 24 Jan 2024 • Yuling Yan, Martin J. Wainwright
In causal inference with panel data under staggered adoption, the goal is to estimate and derive confidence intervals for potential outcomes and treatment effects.
no code implementations • 21 Apr 2023 • Yuling Yan, Weijie J. Su, Jianqing Fan
We demonstrate that an author is incentivized to provide accurate rankings when her utility takes the form of a convex additive function of the adjusted review scores.
no code implementations • 14 Apr 2023 • Gen Li, Yuling Yan, Yuxin Chen, Jianqing Fan
This paper studies reward-agnostic exploration in reinforcement learning (RL) -- a scenario where the learner is unware of the reward functions during the exploration stage -- and designs an algorithm that improves over the state of the art.
no code implementations • 4 Jan 2023 • Yuling Yan, Kaizheng Wang, Philippe Rigollet
Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications.
no code implementations • 8 Jun 2022 • Yuling Yan, Gen Li, Yuxin Chen, Jianqing Fan
This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 14 Mar 2022 • Yuling Yan, Gen Li, Yuxin Chen, Jianqing Fan
This paper is concerned with the asynchronous form of Q-learning, which applies a stochastic approximation scheme to Markovian data samples.
no code implementations • 26 Jul 2021 • Yuling Yan, Yuxin Chen, Jianqing Fan
This paper studies how to construct confidence regions for principal component analysis (PCA) in high dimension, a problem that has been vastly under-explored.
no code implementations • NeurIPS 2021 • Bingyan Wang, Yuling Yan, Jianqing Fan
Our results show that for arbitrarily large-scale MDP, both the model-based approach and Q-learning are sample-efficient when $K$ is relatively small, and hence the title of this paper.
no code implementations • 4 Aug 2020 • Yuxin Chen, Jianqing Fan, Bingyan Wang, Yuling Yan
We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i. e.$~$a sort of random Fourier design and Gaussian design).
no code implementations • NeurIPS 2020 • Kaizheng Wang, Yuling Yan, Mateo Díaz
This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels.
no code implementations • 15 Jan 2020 • Yuxin Chen, Jianqing Fan, Cong Ma, Yuling Yan
This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outliers, and (3) missing data.
no code implementations • 10 Jun 2019 • Yuxin Chen, Jianqing Fan, Cong Ma, Yuling Yan
As a byproduct, we obtain a sharp characterization of the estimation accuracy of our de-biased estimators, which, to the best of our knowledge, are the first tractable algorithms that provably achieve full statistical efficiency (including the preconstant).
no code implementations • 20 Feb 2019 • Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma, Yuling Yan
This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently.
no code implementations • 9 Apr 2017 • Xin Chen, Emma Marriott, Yuling Yan
In recent years, high-speed videoendoscopy (HSV) has significantly aided the diagnosis of voice pathologies and furthered the understanding the voice production in recent years.