10 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…
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…
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…
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…
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…
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…