1 code implementation • 16 Jan 2024 • Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
no code implementations • 17 Oct 2023 • Henry Lam, Zitong Wang
Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization.
no code implementations • 15 Oct 2023 • Haoxian Chen, Henry Lam
Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration.
no code implementations • 24 Jun 2023 • Zhenyuan Liu, Bart P. G. Van Parys, Henry Lam
In data-driven optimization, sample average approximation (SAA) is known to suffer from the so-called optimizer's curse that causes an over-optimistic evaluation of the solution performance.
no code implementations • 16 Jun 2023 • Garud Iyengar, Henry Lam, Tianyu Wang
We develop a general bias correction approach, building on what we call Optimizer's Information Criterion (OIC), that directly approximates the first-order bias and does not require solving any additional optimization problems.
1 code implementation • 28 May 2023 • Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam
Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e. g., few-shot, no repeated observations).
1 code implementation • 13 Apr 2023 • Adam N. Elmachtoub, Henry Lam, Haofeng Zhang, Yunfan Zhao
In this paper, we show that a reverse behavior appears when the model class is well-specified and there is sufficient data.
no code implementations • 3 Dec 2022 • Garud Iyengar, Henry Lam, Tianyu Wang
We propose a simple approach in which the distribution of random perturbations is approximated using a parametric family of distributions.
no code implementations • 22 Oct 2022 • Henry Lam, Kaizheng Wang, Yuhang Wu, Yichen Zhang
We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management.
no code implementations • 21 Oct 2022 • Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, JieLin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
no code implementations • 9 Jun 2022 • Ziyi Huang, Henry Lam, Haofeng Zhang
To overcome these restrictions, we study conditional generative models for aleatoric uncertainty estimation.
no code implementations • 4 Apr 2022 • Mansur Arief, Zhepeng Cen, Zhenyuan Liu, Zhiyuang Huang, Henry Lam, Bo Li, Ding Zhao
In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient IS that is on par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative.
no code implementations • 3 Dec 2021 • Yuanlu Bai, Henry Lam, Svitlana Vyetrenko, Tucker Balch
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks.
1 code implementation • 3 Nov 2021 • Mansur Arief, Yuanlu Bai, Wenhao Ding, Shengyi He, Zhiyuan Huang, Henry Lam, Ding Zhao
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.
no code implementations • 23 Oct 2021 • Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification is at the core of the reliability and robustness of machine learning.
no code implementations • 29 Sep 2021 • Elioth Sanabria, David Yao, Henry Lam
In this paper, we show that even for problems with large state space, when the solution policy of the MDP can be represented by a tree-like structure, our proposed algorithm retrieves a tree of the solution policy of the MDP in computationally tractable time.
no code implementations • 21 Jun 2021 • Yibo Zeng, Henry Lam
In contrast to the hypothesis class complexity in ERM, our DRO bounds depend on the ambiguity set geometry and its compatibility with the true loss function.
1 code implementation • 19 Jun 2021 • Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
no code implementations • 27 May 2021 • Yuanlu Bai, Tucker Balch, Haoxian Chen, Danial Dervovic, Henry Lam, Svitlana Vyetrenko
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
no code implementations • 26 Feb 2021 • Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang
We study the generation of prediction intervals in regression for uncertainty quantification.
no code implementations • 1 Jan 2021 • Ziyi Huang, Henry Lam, Haofeng Zhang
Deep learning has achieved state-of-the-art performance to generate high-quality prediction intervals (PIs) for uncertainty quantification in regression tasks.
no code implementations • 10 Dec 2020 • Haidong Li, Henry Lam, Zhe Liang, Yijie Peng
We consider a context-dependent ranking and selection problem.
Methodology
1 code implementation • 10 Dec 2020 • Haidong Li, Henry Lam, Yijie Peng
We consider a simulation optimization problem for a context-dependent decision-making.
Decision Making Methodology
no code implementations • 10 Oct 2020 • Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests.
no code implementations • 18 Jul 2020 • Jose Blanchet, Henry Lam, Yang Liu, Ruodu Wang
We discuss relevant applications in risk management and economics.
2 code implementations • 28 Jun 2020 • Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.
no code implementations • 14 Oct 2019 • Henry Lam, Fengpei Li, Siddharth Prusty
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}.
no code implementations • ICLR 2020 • YI Zhu, Jing Dong, Henry Lam
We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy.
no code implementations • 19 Apr 2019 • Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao
These Monte Carlo samples are generated from stochastic input models constructed based on real-world data.
no code implementations • 1 Oct 2017 • Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao
The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis.
no code implementations • 19 Oct 2016 • Michael Minyi Zhang, Henry Lam, Lizhen Lin
Effective and accurate model selection is an important problem in modern data analysis.
no code implementations • 8 Jul 2015 • Qinxun Bai, Henry Lam, Stan Sclaroff
We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble.
no code implementations • 23 Jun 2014 • Henry Lam, Zhenming Liu
We consider a non-stochastic online learning approach to price financial options by modeling the market dynamic as a repeated game between the nature (adversary) and the investor.