Optimization
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness
Consequently, standard online convex optimization algorithms do not directly apply.
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, which we say is a "dual drift" caused by dual
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
In this paper, we introduce a generalization of the standard Stackelberg Games (SGs) framework: Calibrated Stackelberg Games (CSGs) .
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
In this paper, we introduce a generalization of the standard Stackelberg Games (SGs) framework: Calibrated Stackelberg Games (CSGs) .
