6 papers · 1 filter
Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
Rachael Hwee Ling Sim, Jue Fan, Xiao Tian +3
Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing data valuation methods…
End-to-End Learning of Correlated Operating Reserve Requirements in Security-Constrained Economic Dispatch
Owen Shen, Hung-po Chao, Haihao Lu +1
Operating reserve requirements in security-constrained economic dispatch (SCED) depend strongly on the assumed correlation structure of renewable forecast errors, yet that structur…
A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
Haoran Xu, Owen Shen, Peter Glynn +2
We study nonstationary Online Linear Programming (OLP), where orders arrive sequentially with reward-resource consumption pairs that form a sequence of independent, but not nec…
Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet
Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given data sources, the goal is to…
A Two-Layer Framework for Joint Online Configuration Selection and Admission Control
Owen Shen, Haoran Xu, Yinyu Ye +2
We study online configuration selection with admission control problem, which arises in LLM serving, GPU scheduling, and revenue management. In a planning horizon with periods,…
Choice-Model-Assisted Q-learning for Delayed-Feedback Revenue Management
Owen Shen, Patrick Jaillet
We study reinforcement learning for revenue management with delayed feedback, where a substantial fraction of value is determined by customer cancellations and modifications observ…