Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Calauzenes, Clement"'
Publikováno v:
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp.7435-7439
In the strategic multi-armed bandit setting, when arms possess perfect information about the player's behavior, they can establish an equilibrium where: 1. they retain almost all of their value, 2. they leave the player with a substantial (linear) re
Externí odkaz:
http://arxiv.org/abs/2408.17101
Inspired by sequential budgeted allocation problems, we study the online matching problem with budget refills. In this context, we consider an online bipartite graph G=(U,V,E), where the nodes in $V$ are discovered sequentially and nodes in $U$ are k
Externí odkaz:
http://arxiv.org/abs/2405.09920
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Preference data, in the form of (complete) rankings in the simplest situation
Externí odkaz:
http://arxiv.org/abs/2303.12878
Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance. This research effort delivered statistically efficient algorithms, improving the regret of previous strategies by exponentially
Externí odkaz:
http://arxiv.org/abs/2201.01985
Publikováno v:
Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021
Finding an optimal matching in a weighted graph is a standard combinatorial problem. We consider its semi-bandit version where either a pair or a full matching is sampled sequentially. We prove that it is possible to leverage a rank-1 assumption on t
Externí odkaz:
http://arxiv.org/abs/2108.00230
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity. In this paper we study GLBs in non-stationary environments, characterized by a general me
Externí odkaz:
http://arxiv.org/abs/2103.05750
Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on maximum likelihood estimation (MLE). While efficient and scalable, ML
Externí odkaz:
http://arxiv.org/abs/2011.09712
Online auctions are one of the most fundamental facets of the modern economy and power an industry generating hundreds of billions of dollars a year in revenue. Auction theory has historically focused on the question of designing the best way to sell
Externí odkaz:
http://arxiv.org/abs/2011.09365
Logistic Bandits have recently attracted substantial attention, by providing an uncluttered yet challenging framework for understanding the impact of non-linearity in parametrized bandits. It was shown by Faury et al. (2020) that the learning-theoret
Externí odkaz:
http://arxiv.org/abs/2010.12642
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online an
Externí odkaz:
http://arxiv.org/abs/2010.10070