Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Zhang, Chicheng"'
We study low-rank matrix trace regression and the related problem of low-rank matrix bandits. Assuming access to the distribution of the covariates, we propose a novel low-rank matrix estimation method called LowPopArt and provide its recovery guaran
Externí odkaz:
http://arxiv.org/abs/2402.11156
Autor:
Li, Yichen, Zhang, Chicheng
We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible. We focus on
Externí odkaz:
http://arxiv.org/abs/2312.16860
Autor:
Li, Yinan, Zhang, Chicheng
We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise~\citep{tsybakov2004optimal} under structured unlabeled data distributions. Inspired by~\cite{diakonikolas2020learning}, we
Externí odkaz:
http://arxiv.org/abs/2310.15411
We study $K$-armed bandit problems where the reward distributions of the arms are all supported on the $[0,1]$ interval. It has been a challenge to design regret-efficient randomized exploration algorithms in this setting. Maillard sampling \cite{mai
Externí odkaz:
http://arxiv.org/abs/2304.14989
In sparse linear bandits, a learning agent sequentially selects an action and receive reward feedback, and the reward function depends linearly on a few coordinates of the covariates of the actions. This has applications in many real-world sequential
Externí odkaz:
http://arxiv.org/abs/2210.15345
Autor:
Li, Yichen, Zhang, Chicheng
Imitation learning (IL) is a general learning paradigm for tackling sequential decision-making problems. Interactive imitation learning, where learners can interactively query for expert demonstrations, has been shown to achieve provably superior sam
Externí odkaz:
http://arxiv.org/abs/2209.12868
We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across multiple r
Externí odkaz:
http://arxiv.org/abs/2206.08556
Autor:
Yan, Tom, Zhang, Chicheng
The fast spreading adoption of machine learning (ML) by companies across industries poses significant regulatory challenges. One such challenge is scalability: how can regulatory bodies efficiently audit these ML models, ensuring that they are fair?
Externí odkaz:
http://arxiv.org/abs/2206.08450
Autor:
Yan, Tom, Zhang, Chicheng
The growing use of machine learning models in consequential settings has highlighted an important and seemingly irreconcilable tension between transparency and vulnerability to gaming. While this has sparked sizable debate in legal literature, there
Externí odkaz:
http://arxiv.org/abs/2202.11266
Heart disease is the number one killer, and ECGs can assist in the early diagnosis and prevention of deadly outcomes. Accurate ECG interpretation is critical in detecting heart diseases; however, they are often misinterpreted due to a lack of trainin
Externí odkaz:
http://arxiv.org/abs/2110.14835