5 citations · 18 across the 33 of their papers we have counts for
8 papers · 1 filter
Linear Convergence in Games with Delayed Feedback via Extra Prediction
Yuma Fujimoto, Kenshi Abe, Kaito Ariu
Feedback delays are inevitable in real-world multi-agent learning. They are known to severely degrade performance, and the convergence rate under delayed feedback is still unclear,…
Consensus Group Relative Policy Optimization for Text Generation
Yuki Ichihara, Yuu Jinnai, Kaito Ariu +1
Many strong decoding methods for text generation follow a sample-and-rerank paradigm: they draw multiple candidates, score each under a utility (reward) function using consensus ac…
Learning from Delayed Feedback in Games via Extra Prediction
Yuma Fujimoto, Kenshi Abe, Kaito Ariu
This study raises and addresses the problem of time-delayed feedback in learning in games. Because learning in games assumes that multiple agents independently learn their strategi…
Matroid Semi-Bandits in Sublinear Time
Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu
We study the matroid semi-bandits problem, where at each round the learner plays a subset of arms from a feasible set, and the goal is to maximize the expected cumulative linea…
Return-Aligned Decision Transformer
Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu +2
Traditional approaches in offline reinforcement learning aim to learn the optimal policy that maximizes the cumulative reward, also known as return. It is increasingly important to…
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.…
The Role of Contextual Information in Best Arm Identification
Masahiro Kato, Kaito Ariu
We study the best-arm identification problem with fixed confidence when contextual (covariate) information is available in stochastic bandits. Although we can use contextual inform…
A Practical Guide of Off-Policy Evaluation for Bandit Problems
Masahiro Kato, Kenshi Abe, Kaito Ariu +1
Off-policy evaluation (OPE) is the problem of estimating the value of a target policy from samples obtained via different policies. Recently, applying OPE methods for bandit proble…