no code implementations • 28 Nov 2023 • Aida Brankovic, Wenjie Huang, David Cook, Sankalp Khanna, Konstanty Bialkowski
The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms.
no code implementations • 4 Nov 2023 • Shijie Pan, Wenjie Huang
A novel Follow-the-Perturbed-Leader type algorithm is proposed and analyzed for solving general long-term constrained optimization problems in online manner, where the objective and constraints are arbitrarily generated and not necessarily convex.
no code implementations • 8 Oct 2023 • Yupeng Wu, Wenjie Huang
The use of reinforcement learning (RL) in practical applications requires considering sub-optimal outcomes, which depend on the agent's familiarity with the uncertain environment.
Distributional Reinforcement Learning reinforcement-learning +1
1 code implementation • 26 Jul 2023 • Tongya Zheng, Tianli Zhang, Qingzheng Guan, Wenjie Huang, Zunlei Feng, Mingli Song, Chun Chen
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
no code implementations • 21 Jun 2023 • Aida Brankovic, David Cook, Jessica Rahman, Wenjie Huang, Sankalp Khanna
The absence of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms.
no code implementations • 3 Jun 2023 • Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang, Haofei Zhang, Yingjie Tian, Yun Su, Mingli Song
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
no code implementations • 8 Aug 2022 • Arindam Bose, Bo Tang, Wenjie Huang, Mojtaba Soltanalian, Jian Li
The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.
no code implementations • 31 Aug 2020 • Jian Wu, William B. Haskell, Wenjie Huang, Huifu Xu
Preference robust choice models concern decision-making problems where the decision maker's (DM) utility/risk preferences are ambiguous and the evaluation is based on the worst-case utility function/risk measure from a set of plausible utility functions/risk measures.
no code implementations • 22 May 2020 • Wenjie Huang, Jing Jiang, Xiao Liu
In this paper, novel gradient-based online learning algorithms are developed to investigate an important environmental application: real-time river pollution source identification, which aims at estimating the released mass, location, and time of a river pollution source based on downstream sensor data monitoring the pollution concentration.
no code implementations • 1 Jul 2018 • Wenjie Huang
We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions.
no code implementations • 17 May 2018 • William B. Haskell, Wenjie Huang, Huifu Xu
Decision maker's preferences are often captured by some choice functions which are used to rank prospects.
no code implementations • 11 May 2018 • Wenjie Huang, William B. Haskell
The inner loop computes the risk by solving a stochastic saddle-point problem.
no code implementations • 11 May 2018 • Wenjie Huang
In this paper, we consider the problem of minimizing the average of a large number of nonsmooth and convex functions.