16 papers
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…
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,…
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…
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…
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…
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…