From the 1 of 4 linked papers with an AI index.
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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…
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…