1 citations · 1 across the 7 of their papers we have counts for
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SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy
Yassine Chemingui, Chenhua Fan, Honghao Wei +1
Safe reinforcement learning typically enforces safety by bounding expected cumulative costs, a criterion that often fails to detect rare but catastrophic tail events. To overcome t…
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
Yuanjie Shi, Peihong Li, Zijian Zhang +2
Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extractors, resources typically unav…
Online Optimization for Offline Safe Reinforcement Learning
Yassine Chemingui, Aryan Deshwal, Alan Fern +2
We study the problem of Offline Safe Reinforcement Learning (OSRL), where the goal is to learn a reward-maximizing policy from fixed data under a cumulative cost constraint. We pro…
Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
Minh Hoang, Azza Fadhel, Aryan Deshwal +2
Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessita…
Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
Yassine Chemingui, Aryan Deshwal, Honghao Wei +2
Offline safe reinforcement learning (OSRL) involves learning a decision-making policy to maximize rewards from a fixed batch of training data to satisfy pre-defined safety constrai…
Non-Myopic Multi-Objective Bayesian Optimization
Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara Engelhardt +2
We consider the problem of finite-horizon sequential experimental design to solve multi-objective optimization (MOO) of expensive black-box objective functions. This problem arises…