220 citations · 251 across the 24 of their papers we have counts for
28 papers
Distributionally Robust Optimization via Targeted Integral Probability Metrics for General Data Processes
Lanran Fang, Jianqiang Cheng, Grani A. Hanasusanto +1
Distributionally robust optimization (DRO) provides a principled framework for decision-making under distributional uncertainty. Classical data-driven DRO frameworks typically cons…
Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification
Duo Zhou, Jorge Chavez, Hesun Chen +2
State-of-the-art neural network (NN) verifiers demonstrate that applying the branch-and-bound (BaB) procedure with fast bounding techniques plays a key role in tackling many challe…
Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions
YoungChul Yoon, Ling Dai, Grani A. Hanasusanto +2
Contextual optimization enhances decision quality by leveraging side information to improve predictions of uncertain parameters. However, existing approaches face significant chall…
A Distributionally Robust Optimization Approach to Quick Response Models under Demand Uncertainty
Panayotis P. Papavassilopoulos, Grani A. Hanasusanto, Yijie Wang
Quick response is a widely adopted strategy to mitigate overproduction in the manufacturing industry, yet recent research reveals a counter-intuitive paradox: while it reduces wast…
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Mingxuan Cui, Duo Zhou, Yuxuan Han +4
Deep reinforcement learning (RL) has achieved remarkable success, yet its deployment in real-world scenarios is often limited by vulnerability to environmental uncertainties. Distr…
Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
Zhuangzhuang Jia, Hyuk Park, Gökçe Dayanıklı +1
Infectious diseases pose major public health challenges to society, highlighting the importance of designing effective policies to reduce economic loss and mortality. In this paper…
Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes
Duo Zhou, Christopher Brix, Grani A Hanasusanto +1
Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advances. However, GCP-CROWN currently relies on gener…
Sample Complexity of Data-driven Multistage Stochastic Programming under Markovian Uncertainty
Hyuk Park, Grani A. Hanasusanto
This work is motivated by the challenges of applying the sample average approximation (SAA) method to multistage stochastic programming with an unknown continuous-state Markov proc…
Generalization Bounds for Contextual Stochastic Optimization using Kernel Regression
Yijie Wang, Grani A. Hanasusanto, Chin Pang Ho
In this paper, we consider contextual stochastic optimization using Nadaraya-Watson kernel regression, which is one of the most common approaches in nonparametric regression. Recen…
Distributionally Robust Performative Optimization
Zhuangzhuang Jia, Yijie Wang, Roy Dong +1
In performative stochastic optimization, decisions can influence the distribution of random parameters, rendering the data-generating process itself decision-dependent. In practice…
Distributionally Robust Optimization with Decision-Dependent Information Discovery
Qing Jin, Angelos Georghiou, Phebe Vayanos +1
We study two-stage distributionally robust optimization (DRO) problems with decision-dependent information discovery (DDID) wherein (a portion of) the uncertain parameters are reve…
Distributionally Fair Stochastic Optimization using Wasserstein Distance
Qing Ye, Grani A. Hanasusanto, Weijun Xie
A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a…
Learning Fair Policies for Multi-stage Selection Problems from Observational Data
Zhuangzhuang Jia, Grani A. Hanasusanto, Phebe Vayanos +1
We consider the problem of learning fair policies for multi-stage selection problems from observational data. This problem arises in several high-stakes domains such as company hir…
Second-order bounds for the M/M/ queue with random arrival rate
Wouter J. E. C. van Eekelen, Grani A. Hanasusanto, John J. Hasenbein +1
Consider an M/M/ queue with the additional feature that the arrival rate is a random variable of which only the mean, variance, and range are known. Using semi-infinite linear p…
Distributionally Robust Path Integral Control
Hyuk Park, Duo Zhou, Grani A. Hanasusanto +1
We consider a continuous-time continuous-space stochastic optimal control problem, where the controller lacks exact knowledge of the underlying diffusion process, relying instead o…
Distributionally Robust Observable Strategic Queues
Yijie Wang, Madhushini Narayana Prasad, Grani A. Hanasusanto +1
This paper presents an extension of Naor's analysis on the join-or-balk problem in observable M/M/1 queues. While all other Markovian assumptions still hold, we explore this proble…
On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach
Prateek R. Srivastava, Yijie Wang, Grani A. Hanasusanto +1
We consider generic stochastic optimization problems in the presence of side information which enables a more insightful decision. The side information constitutes observable exoge…
A Decision Rule Approach for Two-Stage Data-Driven Distributionally Robust Optimization Problems with Random Recourse
Xiangyi Fan, Grani A. Hanasusanto
We study two-stage stochastic optimization problems with random recourse, where the adaptive decisions are multiplied with the uncertain parameters in both the objective function a…
Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration
Yuqi Zhou, Hao Zhu, Grani A. Hanasusanto
Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a…
Wasserstein Robust Classification with Fairness Constraints
Yijie Wang, Viet Anh Nguyen, Grani A. Hanasusanto
We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We…
A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers
Prateek R. Srivastava, Purnamrita Sarkar, Grani A. Hanasusanto
We consider the problem of clustering datasets in the presence of arbitrary outliers. Traditional clustering algorithms such as k-means and spectral clustering are known to perform…
Transmission Switching Under Wind Uncertainty Using Linear Decision Rules
Yuqi Zhou, Hao Zhu, Grani A. Hanasusanto
Increasing penetration of wind and renewable generation poses significant challenges to the power system operations and reliability. This paper considers the real-time optimal tran…
Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming
Guanglin Xu, Grani A. Hanasusanto
We study decision rule approximations for generic multi-stage robust linear optimization problems. We consider linear decision rules for the case when the objective coefficients, t…
Finding Minimum Volume Circumscribing Ellipsoids Using Generalized Copositive Programming
Areesh Mittal, Grani A. Hanasusanto
We study the problem of finding the Lowner-John ellipsoid, i.e., an ellipsoid with minimum volume that contains a given convex set. We reformulate the problem as a generalized copo…