70 citations · 161 across the 37 of their papers we have counts for
39 papers
Rethinking Successive Rejects Algorithms
Mohamed Khair, Junpei Komiyama
In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a simple fixed-budget best-arm i…
Self-Adapting Group of Experts for Multi-Agent Reasoning
Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar +1
Multi-agent systems bring together language model agents with different roles to propose, review, and refine solutions. Each agent's response depends on its model's capabilities, t…
Prefix-Denoising Consistency: Test-Time Verification for Diffusion Language Models
Yuki Ichihara, Naoto Iwase, Mohammad Atif Quamar +1
Diffusion Language Models (DLMs) have recently become increasingly competitive with autoregressive (AR) models, and even outperform them on certain tasks. Unlike AR models, DLMs pr…
SGHA: Evidence-Grounded Research Problem Discovery with Local Language Models
Sarvesh Gharat, Junpei Komiyama
Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experiments, and draft scientific manuscripts. How…
Learning Optimal Dynamic Matching via Graph Neural Networks
Genta Okada, Shunya Noda, Junpei Komiyama +1
Dynamic matching markets require decisions about whom to match and when: matching now yields value but removes participants who may create better future opportunities. We develop a…
Privileged Solutions or Context-Induced Teacher Behavior? Dissecting On-Policy Self-Distillation
Yuki Ichihara, Naoto Iwase, Mohammad Atif Quamar +1
On-Policy Self-Distillation (OPSD) is commonly interpreted as the transfer of privileged information: a teacher observes the verified solution to the target problem and supervises…
Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis
Harsh Shrivastava, Yuta Kawakami, Junpei Komiyama +1
This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an interven…
Regret Analysis of Retry-Based Bandits
Bingkui Tong, Junpei Komiyama, Soichiro Nishimori +1
We provide the first regret analysis of ReMax in stochastic multi-armed bandits. Originally introduced for reinforcement learning, ReMax is motivated by the role of exploration whe…
Reliable Chain-of-Thought via Prefix Consistency
Naoto Iwase, Yuki Ichihara, Mohammad Atif Quamar +1
Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority voting (MV), a test-time techn…
CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
Hirofumi Ota, Naoto Iwase, Yuki Ichihara +2
Large language models often improve reasoning by sampling multiple outputs and aggregating their final answers, but precise and efficient control of error levels remains a challeng…
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…
Rate-optimal Design for Anytime Best Arm Identification
Junpei Komiyama, Kyoungseok Jang, Junya Honda
We consider the best arm identification problem, where the goal is to identify the arm with the highest mean reward from a set of arms under a limited sampling budget. This pro…
Best-of- -- Asymptotic Performance of Test-Time LLM Ensembling
Junpei Komiyama, Daisuke Oba, Masafumi Oyamada
We study best-of- for large language models (LLMs) where the selection is based on majority voting. In particular, we analyze the limit , which we denote as \boinf…
High-dimensional Nonparametric Contextual Bandit Problem
Shogo Iwazaki, Junpei Komiyama, Masaaki Imaizumi
We consider the kernelized contextual bandit problem with a large feature space. This problem involves arms, and the goal of the forecaster is to maximize the cumulative reward…
No-regret incentive-compatible online learning under exact truthfulness with non-myopic experts
Junpei Komiyama, Nishant A. Mehta, Ali Mortazavi
We study an online forecasting setting in which, over rounds, strategic experts each report a forecast to a mechanism, the mechanism selects one forecast, and then the outc…
Data-dependent Bounds with -Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
Quan Nguyen, Shinji Ito, Junpei Komiyama +1
Existing data-dependent and best-of-both-worlds regret bounds for multi-armed bandits problems have limited adaptivity as they are either data-dependent but not best-of-both-worlds…
Fixed Confidence Best Arm Identification in the Bayesian Setting
Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki
We consider the fixed-confidence best arm identification (FC-BAI) problem in the Bayesian setting. This problem aims to find the arm of the largest mean with a fixed confidence lev…
Replicability is Asymptotically Free in Multi-armed Bandits
Junpei Komiyama, Shinji Ito, Yuichi Yoshida +1
We consider a replicable stochastic multi-armed bandit algorithm that ensures, with high probability, that the algorithm's sequence of actions is not affected by the randomness inh…
Learning Fair Division from Bandit Feedback
Hakuei Yamada, Junpei Komiyama, Kenshi Abe +1
This work addresses learning online fair division under uncertainty, where a central planner sequentially allocates items without precise knowledge of agents' values or utilities.…
High-dimensional Contextual Bandit Problem without Sparsity
Junpei Komiyama, Masaaki Imaizumi
In this research, we investigate the high-dimensional linear contextual bandit problem where the number of features is greater than the budget , or it may even be infinite.…
Strategic Choices of Migrants and Smugglers in the Central Mediterranean Sea
Katherine Hoffmann Pham, Junpei Komiyama
The sea crossing from Libya to Italy is one of the world's most dangerous and politically contentious migration routes, and yet over half a million people have attempted the crossi…
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
Junpei Komiyama, Taira Tsuchiya, Junya Honda
We consider the fixed-budget best arm identification problem where the goal is to find the arm of the largest mean with a fixed number of samples. It is known that the probability…
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki
This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats. In medical…
Suboptimal Performance of the Bayes Optimal Algorithm in Frequentist Best Arm Identification
Junpei Komiyama
We consider the fixed-budget best arm identification problem with rewards following normal distributions. In this problem, the forecaster is given arms (or treatments) and …