38 citations · 133 across the 25 of their papers we have counts for
24 papers · 1 filter
Machine Learning. The Science of Selection under Uncertainty
Yevgeny Seldin
Learning, whether natural or artificial, is a process of selection. It starts with a set of candidate options and selects the more successful ones. In the case of machine learning…
Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif +1
PAC-Bayesian analysis is a frequentist framework for incorporating prior knowledge into learning. It was inspired by Bayesian learning, which allows sequential data processing and…
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
Saeed Masoudian, Julian Zimmert, Yevgeny Seldin
We propose a new best-of-both-worlds algorithm for bandits with variably delayed feedback. In contrast to prior work, which required prior knowledge of the maximal delay $d_{\mathr…
Delayed Bandits: When Do Intermediate Observations Help?
Emmanuel Esposito, Saeed Masoudian, Hao Qiu +3
We study a -armed bandit with delayed feedback and intermediate observations. We consider a model where intermediate observations have a form of a finite state, which is observe…
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
Saeed Masoudian, Julian Zimmert, Yevgeny Seldin
We present a modified tuning of the algorithm of Zimmert and Seldin [2020] for adversarial multiarmed bandits with delayed feedback, which in addition to the minimax optimal advers…
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
Chloé Rouyer, Dirk van der Hoeven, Nicolò Cesa-Bianchi +1
We consider online learning with feedback graphs, a sequential decision-making framework where the learner's feedback is determined by a directed graph over the action set. We pres…