arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
By Panayotis Mertikopoulos
arXiv:2606. 06043v1 Announce Type: cross Abstract: Follow-the-regularized-leader framework has shown effectiveness and flexibility in online learning problems, where the choice of learning rates are known to be crucial.
By Jongyeong Lee, Junya Honda, Shinji Ito, Chansoo Kim
arXiv:2610.00911v1 Announce Type: new
Abstract: We study an endogenous nonstationary stochastic bandit problem with latent linear dynamics, where actions affect both immediate rewards and the future...
By Taehyun Hwang, Hyunjun Choi, Heesang Ann, Min-hwan Oh
arXiv:2605.12340v5 Announce Type: replace-cross
Abstract: Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. Real-world deployments require handling...
By Dang Hoang Duy, Yannis Montreuil, Maxime Meyer, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi
arXiv:2510. 22819v3 Announce Type: replace Abstract: The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly important, as it captures the learner's actual decisions and describes the evolution of the learning process over time.
By Jingxin Zhan, Yuze Han, Zhihua Zhang
arXiv:2606. 19891v1 Announce Type: new Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth.
By Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto