Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Jung, Young Hun"'
Autor:
Pfeiffer III, Joseph J., Charles, Denis, Gilton, Davis, Jung, Young Hun, Parsana, Mehul, Anderson, Erik
Today, many web advertising data flows involve passive cross-site tracking of users. Enabling such a mechanism through the usage of third party tracking cookies (3PC) exposes sensitive user data to a large number of parties, with little oversight on
Externí odkaz:
http://arxiv.org/abs/2110.14794
Autor:
Regatti, Jayanth Reddy, Deshmukh, Aniket Anand, Cheng, Frank, Jung, Young Hun, Gupta, Abhishek, Dogan, Urun
Offline reinforcement learning is used to train policies in scenarios where real-time access to the environment is expensive or impossible. As a natural consequence of these harsh conditions, an agent may lack the resources to fully observe the onlin
Externí odkaz:
http://arxiv.org/abs/2110.03165
Alon et al. [2019] and Bun et al. [2020] recently showed that online learnability and private PAC learnability are equivalent in binary classification. We investigate whether this equivalence extends to multi-class classification and regression. Firs
Externí odkaz:
http://arxiv.org/abs/2006.01980
Akademický článek
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Autor:
Yi, Seung-Won, Lee, Han Gyu, Kim, Eunju, Jung, Young-Hun, Bok, Eun-Yeong, Cho, Ara, Do, Yoon Jung, So, Kyoung-Min, Hur, Tai-Young, Oh, Sang-Ik
Publikováno v:
In Veterinary and Animal Science December 2023 22
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides. We propose a novel surrogate loss function and unbiased estimator, allow
Externí odkaz:
http://arxiv.org/abs/1910.10937
Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algorithms over the unknown reward distributions and prove a sub-linear, $O(
Externí odkaz:
http://arxiv.org/abs/1910.05654
Autor:
Jung, Young Hun, Tewari, Ambuj
Restless bandit problems are instances of non-stationary multi-armed bandits. These problems have been studied well from the optimization perspective, where the goal is to efficiently find a near-optimal policy when system parameters are known. Howev
Externí odkaz:
http://arxiv.org/abs/1905.12673
Autor:
Yang, Hye In, Naveen, Kenkera Rayappa, Cho, Sang Min, Kim, Jin Young, Jung, Young Hun, Kwon, Jang Hyuk
Publikováno v:
In Organic Electronics April 2023 115
Akademický článek
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