convergence analysis 1distribution shift 1finite-difference estimation 1gradient-based optimization 1performative prediction 1
From the 1 of 3 linked papers with an AI index.
3 papers
math.OC2026
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada +1
The paper proposes a gradient-based optimization algorithm that estimates distribution shifts via finite differences, providing convergence guarantees for a wider range of loss fun…
math.OC2025
Zeroth-order gradient estimators for stochastic problems with decision-dependent distributions
Yuya Hikima, Akiko Takeda
Stochastic optimization problems with unknown decision-dependent distributions have attracted increasing attention in recent years due to its importance in applications. Since the…
math.OC2024
Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions
Yuya Hikima, Akiko Takeda
In this study, we consider an optimization problem with uncertainty dependent on decision variables, which has recently attracted attention due to its importance in machine learnin…