5 papers
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada +1
We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been pro…
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…
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…
Joint Pricing and Matching for Resource Allocation Platforms via Min-cost Flow Problem
Yuya Hikima, Yasunori Akagi, Hideaki Kim
Stochastic matching is the stochastic version of the well-known matching problem, which consists in maximizing the rewards of a matching under a set of probability distributions as…
Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty
Yuya Hikima, Akiko Takeda
Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previo…