no code implementations • 29 Mar 2024 • Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang
We characterize the sharp bounds on policy value under this model, that is, the tightest possible bounds given by the transition observations from the original MDP, and we study the estimation of these bounds from such transition observations.
no code implementations • 23 Mar 2024 • Kaiwen Wang, Yinzhe Shen, Martin Lauer
Existing object detectors encounter challenges in handling domain shifts between training and real-world data, particularly under poor visibility conditions like fog and night.
no code implementations • 10 Mar 2024 • Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
We study Risk-Sensitive Reinforcement Learning (RSRL) with the Optimized Certainty Equivalent (OCE) risk, which generalizes Conditional Value-at-risk (CVaR), entropic risk and Markowitz's mean-variance.
no code implementations • 8 Mar 2024 • Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
no code implementations • 11 Feb 2024 • Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
Second-order bounds are instance-dependent bounds that scale with the variance of return, which we prove are tighter than the previously known small-loss bounds of distributional RL.
Distributional Reinforcement Learning Multi-Armed Bandits +1
1 code implementation • 21 Jul 2023 • Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query optimization.
no code implementations • NeurIPS 2023 • Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
In online RL, we propose a DistRL algorithm that constructs confidence sets using maximum likelihood estimation.
no code implementations • 7 Feb 2023 • Kaiwen Wang, Nathan Kallus, Wen Sun
In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing on the objective of Conditional Value at Risk (CVaR) with risk tolerance $\tau$.
1 code implementation • 12 Jul 2022 • Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun
We study representation learning for Offline Reinforcement Learning (RL), focusing on the important task of Offline Policy Evaluation (OPE).
1 code implementation • 29 May 2022 • Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang
We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a \emph{target task}.
no code implementations • 18 Mar 2022 • Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov
Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signal about the missing confounders.
1 code implementation • 19 Feb 2022 • Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou
Thanks to a localization technique, LDR$^2$OPE only requires fitting a small number of regressions, just like DR methods for standard OPE.
no code implementations • 14 May 2021 • Cheolhei Lee, Kaiwen Wang, Jianguo Wu, Wenjun Cai, Xiaowei Yue
Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems.
1 code implementation • 19 Dec 2020 • Kaiwen Wang, Travis Dick, Maria-Florina Balcan
We provide the first utility guarantees for differentially private top-down decision tree learning in both the single machine and distributed settings.
1 code implementation • 12 Dec 2020 • Yinan Wang, Kaiwen Wang, Wenjun Cai, Xiaowei Yue
Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems.
no code implementations • 31 May 2018 • Guannan Zhao, Bo Zhou, Kaiwen Wang, Rui Jiang, Min Xu
The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept.
no code implementations • 16 May 2018 • Chang Liu, Xiangrui Zeng, Kaiwen Wang, Qiang Guo, Min Xu
Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells.