70 citations · 161 across the 37 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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.…
Rate-optimal Bayesian Simple Regret in Best Arm Identification
Junpei Komiyama, Kaito Ariu, Masahiro Kato +1
We consider best arm identification in the multi-armed bandit problem. Assuming certain continuity conditions of the prior, we characterize the rate of the Bayesian simple regret.…