8 citations · 27 across the 20 of their papers we have counts for
25 papers
Fractional Assignment with Preferences
Yasushi Kawase, Warut Suksompong, Hanna Sumita +1
We study a fractional assignment setting where objects are to be assigned to agents with unit capacity, and each agent specifies an ideal distribution over the objects. Unl…
Witness-Certified Fair Division with Comparison Queries
Tatsuhito Yamagata, Hanna Sumita
We study fair division of indivisible goods when agents' valuations are accessed only through ordinal comparisons between bundles, with arbitrary tie-breaking. In this model, even…
Decomposition Envy-Freeness in Random Assignment
Yasushi Kawase, Warut Suksompong, Hanna Sumita +1
In random assignment, fairness is often captured by stochastic-dominance envy-freeness (SD-EF). We observe that assignments satisfying SD-EF may admit decompositions that result in…
Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret
Tsubasa Harada, Yasushi Kawase, Hanna Sumita
We study the repeated optimal stopping problem, in which the same optimal stopping instance with an unknown distribution is solved repeatedly over rounds. We aim to simultaneou…
Two-Sided Fairness in Many-to-One Matching
Ayumi Igarashi, Naoyuki Kamiyama, Yasushi Kawase +3
We consider a classic many-to-one matching setting, where participants need to be assigned to teams based on the preferences of both sides. Unlike most of the matching literature,…
Scheduling on Identical Machines with Setup Time and Unknown Execution Time
Yasushi Kawase, Kazuhisa Makino, Vinh Long Phan +1
In this study, we investigate a scheduling problem on identical machines in which jobs require initial setup before execution. We assume that an algorithm can dynamically form a ba…
Bandit Max-Min Fair Allocation
Tsubasa Harada, Shinji Ito, Hanna Sumita
In this paper, we study a new decision-making problem called the bandit max-min fair allocation (BMMFA) problem. The goal of this problem is to maximize the minimum utility among a…
A general framework for finding diverse solutions via network flow and its applications
Yuni Iwamasa, Tomoki Matsuda, Shunya Morihira +1
In this paper, we present a general framework for efficiently computing diverse solutions to combinatorial optimization problems. Given a problem instance, the goal is to find …
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem
Koji Ichikawa, Shinji Ito, Daisuke Hatano +4
We consider the sparse contextual bandit problem where arm feature affects reward through the inner product of sparse parameters. Recent studies have developed sparsity-agnostic al…
Towards Optimal Subsidy Bounds for Envy-freeable Allocations
Yasushi Kawase, Kazuhisa Makino, Hanna Sumita +2
We study the fair division of indivisible items with subsidies among agents, where the absolute marginal valuation of each item is at most one. Under monotone valuations (where…
Fair Allocation with Binary Valuations for Mixed Divisible and Indivisible Goods
Yasushi Kawase, Koichi Nishimura, Hanna Sumita
The fair allocation of mixed goods, consisting of both divisible and indivisible goods, has been a prominent topic of study in economics and computer science. We define an allocati…
Envy-freeness and maximum Nash welfare for mixed divisible and indivisible goods
Koichi Nishimura, Hanna Sumita
We study fair allocation of resources consisting of both divisible and indivisible goods to agents with additive valuations. When only divisible or indivisible goods exist, it is k…
Stochastic Solutions for Dense Subgraph Discovery in Multilayer Networks
Yasushi Kawase, Atsushi Miyauchi, Hanna Sumita
Network analysis has played a key role in knowledge discovery and data mining. In many real-world applications in recent years, we are interested in mining multilayer networks, whe…
Random Assignment of Indivisible Goods under Constraints
Yasushi Kawase, Hanna Sumita, Yu Yokoi
We investigate the problem of random assignment of indivisible goods, in which each agent has an ordinal preference and a constraint. Our goal is to characterize the conditions und…
Fair Division with Two-Sided Preferences
Ayumi Igarashi, Yasushi Kawase, Warut Suksompong +1
We study a fair division setting in which participants are to be fairly distributed among teams, where not only do the teams have preferences over the participants as in the canoni…
Online Task Assignment Problems with Reusable Resources
Hanna Sumita, Shinji Ito, Kei Takemura +4
We study online task assignment problem with reusable resources, motivated by practical applications such as ridesharing, crowdsourcing and job hiring. In the problem, we are given…
Online Max-min Fair Allocation
Yasushi Kawase, Hanna Sumita
We study an online version of the max-min fair allocation problem for indivisible items. In this problem, items arrive one by one, and each item must be allocated irrevocably on ar…
Fair and Truthful Mechanism with Limited Subsidy
Hiromichi Goko, Ayumi Igarashi, Yasushi Kawase +5
The notion of \emph{envy-freeness} is a natural and intuitive fairness requirement in resource allocation. With indivisible goods, such fair allocations are unfortunately not guara…
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions
Kei Takemura, Shinji Ito, Daisuke Hatano +4
The contextual combinatorial semi-bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each…
Non-zero-sum Stackelberg Budget Allocation Game for Computational Advertising
Daisuke Hatano, Yuko Kuroki, Yasushi Kawase +3
Computational advertising has been studied to design efficient marketing strategies that maximize the number of acquired customers. In an increased competitive market, however, a m…
Causal Bandits with Propagating Inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita +4
Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm and obtains an observation corresponding to . Theore…
Randomized Strategies for Robust Combinatorial Optimization
Yasushi Kawase, Hanna Sumita
In this paper, we study the following robust optimization problem. Given an independence system and candidate objective functions, we choose an independent set, and then an adversa…
Submodular maximization with uncertain knapsack capacity
Yasushi Kawase, Hanna Sumita, Takuro Fukunaga
We consider the maximization problem of monotone submodular functions under an uncertain knapsack constraint. Specifically, the problem is discussed in the situation that the knaps…
Optimal Matroid Partitioning Problems
Yasushi Kawase, Kei Kimura, Kazuhisa Makino +1
This paper studies optimal matroid partitioning problems for various objective functions. In the problem, we are given a finite set and weighted matroids $(E, \mathcal{I}_i…