Publishing type：Research paper (international conference proceedings)  

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Other Link： https://openreview.net/forum?id=ULAQ9GmJlo

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.ejor.2024.12.023

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Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Publisher：AAAI Press  

DOI： 10.1609/aaai.v39i16.33890

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Other Link： https://dblp.org/db/conf/aaai/aaai2025.html#HikimaT25

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (international conference proceedings)  

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Other Link： https://openreview.net/forum?id=cRmuEY7jhb

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Institute of Mathematical Sciences (AIMS)  

DOI： 10.3934/jimo.2023022

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We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been proposed. However, they typically assume specific data distributions or loss functions, which limit their practical applicability. To overcome these limitations, we propose a gradient-based optimization method with convergence guarantees under substantially weaker assumptions. Our method explicitly estimates the induced distribution shift through finite differences. It enables higher-dimensional optimization across broader classes of loss functions and data distributions. We also propose a practical variant that reduces the number of samples required. Numerical experiments demonstrate that our proposed algorithms converge faster and more consistently than existing ones.

arXiv

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Other Link： https://arxiv.org/pdf/2607.26562v1

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 associated with the nodes and edges. In most stochastic matching problems, the probability distributions inherent in the nodes and edges are set a priori and are not controllable. However, many resource allocation platforms can control the probability distributions by changing prices. For example, a rideshare platform can control the distribution of the number of requesters by setting the fare to maximize the reward of a taxi-requester matching. Although several methods for optimizing price have been developed, optimizations in consideration of the matching problem are still in its infancy. In this paper, we tackle the problem of optimizing price in the consideration of the resulting bipartite graph matching, given the effect of the price on the probabilistic uncertainty in the graph. Even though our problem involves hard to evaluate objective values and is non-convex, we construct a (1-1/e)-approximation algorithm under the assumption that a convex min-cost flow problem can be solved exactly.

arXiv

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Other Link： https://arxiv.org/pdf/2404.19241v1

Presentation type：Oral presentation (invited, special)  

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