2017/03/05 by Guy Uziel, Uziel, Guy, Ran El‐Yaniv +1 · 2 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Advanced Control Systems Optimization #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.1703.01680
openalex publication_date 2017/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered simultaneously. Recently, an algorithm for dealing with several objective functions in the i.i.d. case has been presented. In this paper, we extend the multi-objective framework to the case of stationary and ergodic processes, thus allowing dependencies among observations. We first identify an asymptomatic lower bound for any prediction strategy and then present an algorithm whose predictions achieve the optimal solution while fulfilling any continuous and convex constraining criterion.