Mortal MAB, Multi-Armed Bandit
Related
Mortal Multi Armed Bandit (2008)
References
https://papers.nips.cc/paper/2008/file/788d986905533aba051261497ecffcbb-Paper.pdf
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Mortal MAB, Multi-Armed Bandit
Mortal Multi Armed Bandit (2008)
https://papers.nips.cc/paper/2008/file/788d986905533aba051261497ecffcbb-Paper.pdf
Mortal MAB Related Reference: Mortal Multi-Armed Bandits
Introduction Thompson sampling is an old heuristic that has a spirit of Bayesian inference and selects an arm based on posterior samples of the expectation of each arm.
Links Paper link Abstract optimism principle 에 기반한 알고리즘은 문제에 대한 구조를 exploit 하는데 실패하여 점근적으로 suboptimal 결과를 보임 context 분포와 exploration policy 가 나눠지도록 (decoupled) regret lower...
Metadata Tag: Contextual Bandit, Thompson sampling, LinUCB, Multi-Armed Bandit Link: towardsdatascience.com/recommender-systems-using-linucb-a-contextual-multi-armed-bandit-appr...
Abstract Probability Ranking Principle 에 의하면 top-K items 을 greedy 하게 rank 하는것이 optimal 함 대신 Introduction This work investigates how the “equity of exposure” principle can be...
References www.chenshouyuan.com/papers/sdm14.pdf .
Abstract 해결하려는 문제: a non-stationary and context-free Multi-Armed Bandit problem, 유저나 아이템에 대한 어떠한 정보가 없는 경우 Introduction 사용자의 행동을 두가지 형태로 나눔: loyal, curious Comparing to prior...
Summary 이 논문의 main research question: how approximated model posteriors affect the performance of decision making via Thompson Sampling in contextual bandits.
Abstract 사용자와 콘텐츠 정보를 활용한 개인화 웹 서비스 (광고, 뉴스 등) 를 제공하는 것은 다음과 같은 두 가지 이유로 어렵다.
Dueling Bandit Gradient Descent Related papers/recommender system/Online learning to rank for information retrieval Multileave Gradient Descent.
Mortal MAB Related Reference: Mortal Multi-Armed Bandits Body: 논문을 간략하게 읽고 다시 정리해보는 것이 좋을 것 같다.