Results 61 to 70 of about 497,803 (209)
Empirical Likelihood for Contextual Bandits
CoRR, 2019 Accepted at NeurIPS ...
Nikos Karampatziakis +2 more
openaire +4 more sources
Respected and Ignored: British Women and the Arab Revolt (1936–39) in Mandate Palestine
Gender &History, EarlyView.ABSTRACT This article aims to reinsert British women and their perspectives into our understanding of anticolonial resistance in Palestine. Historiography on the Arab Revolt (1936–39) has previously been built from colonial state archives that disproportionally speak with a male voice and, as such, rely on certain gendered assumptions to describe the ...
Alex Worsfold
wiley +1 more source
Learning Exploration for Contextual Bandit
, 2016 International audienceWe study here the problem of learning the exploration exploitation trad-off in the contextual bandit problem with linear reward function setting.
Bouneffouf, Djallel, Claeys, Emmanuelle
core +1 more source
Comparison of Video Recommendation Effects of Etc, Ucb, and Thompson Sampling Algorithms on Short-Video Platforms [PDF]
ITM Web of ConferencesThis paper comprehensively compares the performance of three multi-armed bandit (MAB) algorithms, Epsilon-Then-Commit (ETC), upper confidence bound (UCB), and Thompson sampling (TS), for video recommendation in dynamic environments.
Li Shouchuan
doaj +1 more source
History, EarlyView.Abstract This article examines the poll tax (Ottoman Turkish cizye; Arabic jizya) levied on non‐Muslim subjects of the Ottoman Empire, not as a fixed, one‐dimensional tax item within the Ottoman fiscal system but as a multi‐layered administrative instrument organized around obligation, exemption and remission.
ÖZLEM BAŞARIR
wiley +1 more source
Contextual Bandits with Similarity Information
J. Mach. Learn. Res., 2009 In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a time-invariant set of alternatives and receives the payoff associated with this alternative. While the case of small strategy sets is by now well-understood, a lot of recent work has focused on MAB problems with exponentially or ...
openaire +5 more sources
The British Journal of Sociology, EarlyView.ABSTRACT Tracing the early adoption of computer gang databases by the Los Angeles County Sheriff's Department and the Los Angeles Police Department in the 1980s to the deployment of computationally‐assisted surveillance during the Vietnam War, this paper uses a genealogical approach to compare surveillance technologies developed across the arc of ...
Christina Hughes
wiley +1 more source
Linear Contextual Bandits with Interference
CoRRInterference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings, where multiple units are present in the same round, potential interference can significantly affect the estimation of expected rewards for different arms ...
Yang Xu 0089, Wenbin Lu, Rui Song 0006
openaire +3 more sources
Encrypted Linear Contextual Bandit
, 2021 Contextual bandit is a general framework for online learning in sequential decision-making problems that has found application in a wide range of domains, including recommendation systems, online advertising, and clinical trials. A critical aspect of bandit methods is that they require to observe the contexts --i.e., individual or group-level data ...
Garcelon, Evrard +2 more
openaire +4 more sources
A generalised gittins index for a class of multi-armed bandits with general resource requirements [PDF]
, 2009 We generalise classical multiarmed bandits to allow for the distribution of a (fixed amount of a) divisible resource among the constituent bandits at each decision point.
Minty, R J, Glazebrook, K D
core +4 more sources