Zobrazeno 1 - 10
of 6 347
pro vyhledávání: '"A Zeevi"'
This paper considers a finite horizon optimal stopping problem for a sequence of independent and identically distributed random variables. The objective is to design stopping rules that attempt to select the random variable with the highest value in
Externí odkaz:
http://arxiv.org/abs/2404.12949
In data exploration, executing complex non-aggregate queries over large databases can be time-consuming. Our paper introduces a novel approach to address this challenge, focusing on finding an optimized subset of data, referred to as the approximatio
Externí odkaz:
http://arxiv.org/abs/2401.17059
We propose a new regret minimization algorithm for episodic sparse linear Markov decision process (SMDP) where the state-transition distribution is a linear function of observed features. The only previously known algorithm for SMDP requires the know
Externí odkaz:
http://arxiv.org/abs/2310.15286
Autor:
Xu, Yunbei, Zeevi, Assaf
We develop a general theory to optimize the frequentist regret for sequential learning problems, where efficient bandit and reinforcement learning algorithms can be derived from unified Bayesian principles. We propose a novel optimization approach to
Externí odkaz:
http://arxiv.org/abs/2310.00806
Due to numerous applications in retail and (online) advertising the problem of assortment selection has been widely studied under many combinations of discrete choice models and feasibility constraints. In many situations, however, an assortment of p
Externí odkaz:
http://arxiv.org/abs/2308.05207
In a recent work, Laforgue et al. introduce the model of last switch dependent (LSD) bandits, in an attempt to capture nonstationary phenomena induced by the interaction between the player and the environment. Examples include satiation, where consec
Externí odkaz:
http://arxiv.org/abs/2306.00338
We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vector is a linear function of the context. PFILin includes the best arm
Externí odkaz:
http://arxiv.org/abs/2306.00096
We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget vector and the total value is as large as possible. We consider the se
Externí odkaz:
http://arxiv.org/abs/2301.13791
Autor:
Kalvit, Anand, Zeevi, Assaf
We consider a stochastic multi-armed bandit (MAB) problem motivated by ``large'' action spaces, and endowed with a population of arms containing exactly $K$ arm-types, each characterized by a distinct mean reward. The decision maker is oblivious to t
Externí odkaz:
http://arxiv.org/abs/2301.07243
Autor:
Jakob Sommer, Fiona Dierksen, Tal Zeevi, Anh Tuan Tran, Emily W. Avery, Adrian Mak, Ajay Malhotra, Charles C. Matouk, Guido J. Falcone, Victor Torres-Lopez, Sanjey Aneja, James Duncan, Lauren H. Sansing, Kevin N. Sheth, Seyedmehdi Payabvash
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
PurposeComputed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after
Externí odkaz:
https://doaj.org/article/c14bcc3560d9493088e70665dce648f8