Search Results for author: Henry Lam

Found 35 papers, 7 papers with code

Mallows-DPO: Fine-Tune Your LLM with Preference Dispersions

no code implementations23 May 2024 Haoxian Chen, Hanyang Zhao, Henry Lam, David Yao, Wenpin Tang

Direct Preference Optimization (DPO) has recently emerged as a popular approach to improve reinforcement learning with human feedback (RLHF), leading to better techniques to fine-tune large language models (LLM).

Bagging Improves Generalization Exponentially

no code implementations23 May 2024 Huaqian Jie, Donghao Ying, Henry Lam, Wotao Yin

Bagging is a popular ensemble technique to improve the accuracy of machine learning models.

Stochastic Optimization

Learning from Sparse Offline Datasets via Conservative Density Estimation

1 code implementation16 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.

D4RL Density Estimation +2

Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification

no code implementations17 Oct 2023 Henry Lam, Zitong Wang

Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization.

Stochastic Optimization Uncertainty Quantification

Pseudo-Bayesian Optimization

no code implementations15 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.

Bayesian Optimization Uncertainty Quantification

Smoothed $f$-Divergence Distributionally Robust Optimization

no code implementations24 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.

Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization

no code implementations16 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.

Model Selection

Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes

1 code implementation28 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).

Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework

no code implementations3 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.

Generalization Bounds Management +2

Adaptive Data Fusion for Multi-task Non-smooth Optimization

no code implementations22 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.

Decision Making Management

Evaluating Aleatoric Uncertainty via Conditional Generative Models

no code implementations9 Jun 2022 Ziyi Huang, Henry Lam, Haofeng Zhang

To overcome these restrictions, we study conditional generative models for aleatoric uncertainty estimation.

Uncertainty Quantification

Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling

no code implementations4 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.

Autonomous Vehicles

Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization

no code implementations3 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.

Bayesian Optimization Time Series Analysis

Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

1 code implementation3 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.

Quantifying Epistemic Uncertainty in Deep Learning

no code implementations23 Oct 2021 Ziyi Huang, Henry Lam, Haofeng Zhang

Uncertainty quantification is at the core of the reliability and robustness of machine learning.

Uncertainty Quantification

Decision Tree Algorithms for MDP

no code implementations29 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.

Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization

no code implementations21 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.

BIG-bench Machine Learning Generalization Bounds

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

1 code implementation19 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.

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set

no code implementations27 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.

Conditional Coverage Estimation for High-quality Prediction Intervals

no code implementations1 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.

Prediction Intervals Uncertainty Quantification +1

Context-dependent Ranking and Selection under a Bayesian Framework

no code implementations10 Dec 2020 Haidong Li, Henry Lam, Zhe Liang, Yijie Peng

We consider a context-dependent ranking and selection problem.

Methodology

Efficient Learning for Clustering and Optimizing Context-Dependent Designs

1 code implementation10 Dec 2020 Haidong Li, Henry Lam, Yijie Peng

We consider a simulation optimization problem for a context-dependent decision-making.

Decision Making Methodology

Rare-Event Simulation for Neural Network and Random Forest Predictors

no code implementations10 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.

BIG-bench Machine Learning

Convolution Bounds on Quantile Aggregation

no code implementations18 Jul 2020 Jose Blanchet, Henry Lam, Yang Liu, Ruodu Wang

We discuss relevant applications in risk management and economics.

Management

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems

2 code implementations28 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.

Robust Importance Weighting for Covariate Shift

no code implementations14 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}.

Uncertainty Quantification and Exploration for Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL) +2

Evaluation Uncertainty in Data-Driven Self-Driving Testing

no code implementations19 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.

Autonomous Vehicles

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

no code implementations1 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.

Robust and Parallel Bayesian Model Selection

no code implementations19 Oct 2016 Michael Minyi Zhang, Henry Lam, Lizhen Lin

Effective and accurate model selection is an important problem in modern data analysis.

Model Selection Variable Selection

A Bayesian Approach for Online Classifier Ensemble

no code implementations8 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.

From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments

no code implementations23 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.

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