20 citations · 43 across the 31 of their papers we have counts for
33 papers
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,…
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…
Learning to detect continuous gravitational waves: an open data-analysis competition
Rodrigo Tenorio, Michael J. Williams, Joseph Bayley +29
We report results of a public data-analysis challenge, hosted on the open data-science platform Kaggle, to detect simulated continuous gravitational-wave signals (CWs). These are w…
Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently
Kenshin Abe, Yunzhuo Wang, Shuhei Watanabe
Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools. Since these HPO tools have been developed in line wit…
Asymmetric Perturbation in Solving Bilinear Saddle-Point Optimization
Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu +1
This paper proposes asymmetric perturbation, where only one player's payoff function is perturbed, for solving bilinear saddle-point optimization problems, commonly arising in mini…
On the Power of Perturbation under Sampling in Solving Extensive-Form Games
Wataru Masaka, Mitsuki Sakamoto, Kenshi Abe +3
We investigate how perturbation does and does not improve the Follow-the-Regularized-Leader (FTRL) algorithm in solving imperfect-information extensive-form games under sampling, w…
Approximate State Abstraction for Markov Games
Hiroki Ishibashi, Kenshi Abe, Atsushi Iwasaki
This paper introduces state abstraction for two-player zero-sum Markov games (TZMGs), where the payoffs for the two players are determined by the state representing the environment…
Time-Varyingness in Auction Breaks Revenue Equivalence
Yuma Fujimoto, Kaito Ariu, Kenshi Abe
Auction is applied for trade with various mechanisms. A simple but practical question is which mechanism, typically first-price or second-price auctions, is preferred from the pers…
Last Iterate Convergence in Monotone Mean Field Games
Noboru Isobe, Kenshi Abe, Kaito Ariu
In the Lasry--Lions framework, Mean-Field Games (MFGs) model interactions among an infinite number of agents. However, existing algorithms either require strict monotonicity or onl…
Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games
Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu +1
This paper presents a payoff perturbation technique, introducing a strong convexity to players' payoff functions in games. This technique is specifically designed for first-order m…
Synchronization in Learning in Periodic Zero-Sum Games Triggers Divergence from Nash Equilibrium
Yuma Fujimoto, Kaito Ariu, Kenshi Abe
Learning in zero-sum games studies a situation where multiple agents competitively learn their strategy. In such multi-agent learning, we often see that the strategies cycle around…
Global Behavior of Learning Dynamics in Zero-Sum Games with Memory Asymmetry
Yuma Fujimoto, Kaito Ariu, Kenshi Abe
This study examines the global behavior of dynamics in learning in games between two players, X and Y. We consider the simplest situation for memory asymmetry between two players:…
Filtered Direct Preference Optimization
Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai +2
Reinforcement learning from human feedback (RLHF) plays a crucial role in aligning language models with human preferences. While the significance of dataset quality is generally re…
Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment
Yuu Jinnai, Tetsuro Morimura, Kaito Ariu +1
Best-of-N (BoN) sampling with a reward model has been shown to be an effective strategy for aligning Large Language Models (LLMs) to human preferences at the time of decoding. BoN…
Nash Equilibrium and Learning Dynamics in Three-Player Matching -Action Games
Yuma Fujimoto, Kaito Ariu, Kenshi Abe
Learning in games discusses the processes where multiple players learn their optimal strategies through the repetition of game plays. The dynamics of learning between two players i…
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…
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.…
Model-Based Minimum Bayes Risk Decoding for Text Generation
Yuu Jinnai, Tetsuro Morimura, Ukyo Honda +2
Minimum Bayes Risk (MBR) decoding has been shown to be a powerful alternative to beam search decoding in a variety of text generation tasks. MBR decoding selects a hypothesis from…
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative
Sho Shimoyama, Tetsuro Morimura, Kenshi Abe +6
Dialog policies, which determine a system's action based on the current state at each dialog turn, are crucial to the success of the dialog. In recent years, reinforcement learning…
Exploration of Unranked Items in Safe Online Learning to Re-Rank
Hiroaki Shiino, Kaito Ariu, Kenshi Abe +1
Bandit algorithms for online learning to rank (OLTR) problems often aim to maximize long-term revenue by utilizing user feedback. From a practical point of view, however, such algo…
Adaptively Perturbed Mirror Descent for Learning in Games
Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto +1
This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone in the strategy profile sp…
Memory Asymmetry Creates Heteroclinic Orbits to Nash Equilibrium in Learning in Zero-Sum Games
Yuma Fujimoto, Kaito Ariu, Kenshi Abe
Learning in games considers how multiple agents maximize their own rewards through repeated games. Memory, an ability that an agent changes his/her action depending on the history…
Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium
Yuma Fujimoto, Kaito Ariu, Kenshi Abe
Repeated games consider a situation where multiple agents are motivated by their independent rewards throughout learning. In general, the dynamics of their learning become complex.…
Fair Matrix Factorisation for Large-Scale Recommender Systems
Riku Togashi, Kenshi Abe
Recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue in p…