9 papers
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…
Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimization
Junhui Zhang, Patrick Jaillet
We propose two variants of the Primal Dual Hybrid Gradient (PDHG) algorithm for saddle point problems with block decomposable duals, hereafter called Multi-Timescale PDHG (MT-PDHG)…
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…
Efficient Online Mirror Descent Stochastic Approximation for Multi-Stage Stochastic Programming
Junhui Zhang, Patrick Jaillet
We study the unconstrained and the minimax saddle point variants of the convex multi-stage stochastic programming problem, where consecutive decisions are coupled through the objec…
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…