220 citations · 251 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…