Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Elahi, Sepehr"'
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies. Observational studies can suffer from unmeasured con
Externí odkaz:
http://arxiv.org/abs/2407.05330
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: li
Externí odkaz:
http://arxiv.org/abs/2403.09300
In federated multi-armed bandit problems, maximizing global reward while satisfying minimum privacy requirements to protect clients is the main goal. To formulate such problems, we consider a combinatorial contextual bandit setting with groups and ch
Externí odkaz:
http://arxiv.org/abs/2111.14778
We consider a contextual bandit problem with a combinatorial action set and time-varying base arm availability. At the beginning of each round, the agent observes the set of available base arms and their contexts and then selects an action that is a
Externí odkaz:
http://arxiv.org/abs/2110.02248
We consider the problem of optimizing a vector-valued objective function $\boldsymbol{f}$ sampled from a Gaussian Process (GP) whose index set is a well-behaved, compact metric space $({\cal X},d)$ of designs. We assume that $\boldsymbol{f}$ is not k
Externí odkaz:
http://arxiv.org/abs/2006.14061
Publikováno v:
In Optics and Lasers in Engineering September 2023 168
Akademický článek
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Akademický článek
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Publikováno v:
MicroTAS 2020-24th International Conference on Miniaturized Systems for Chemistry and Life Sciences
Date of Conference: 4-9 October 2020 Conference Name: 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2020 The ability to measure physical properties of droplets in real-time is required to design preci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3533::79ef13b4a75efd9585b11aaaf5ee7f60
https://hdl.handle.net/11693/75840
https://hdl.handle.net/11693/75840
We consider contextual combinatorial volatile multi-armed bandit (CCV-MAB), in which at each round, the learner observes a set of available base arms and their contexts, and then, selects a super arm that contains K base arms in order to maximize its
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=r39c86a4b39b::223703f55be958a3a5eb05e8751e25ef
https://aperta.ulakbim.gov.tr/record/10923
https://aperta.ulakbim.gov.tr/record/10923