Augment Decentralized Online Convex Optimization with Arbitrarily Bad Machine-Learned Predictions

Decentralized online convex optimization (DOCO), as a pivotal computational paradigm in machine learning, has been applied to many critical tasks. However, existing DOCO algorithms, due to their excessive emphasis on the worst-case theoretical performance, appear to be overly cautious in making decisions across all possible cases, especially in real-world applications where the worst cases actually hardly occur. Therefore, these existing approaches typically are limited in performance in practice. To avoid such pessimistic strategies, we propose to study the approach of augmenting DOCO with machine-learned predictions that can guide the decision-making process. We present an overview of the problem along with the preliminary results and outlook in this work.