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Mortal Multi-Armed Bandits

Mortal Multi-Armed Bandits

2026년 6월 14일1 min read

Mortal Multi-Armed Bandits

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 Multi Armed Bandit (2008)

Mortal Multi Armed Bandit (2008) Mortal MAB Related Reference: Mortal Multi-Armed Bandits

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An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits

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...

Recommender systems using LinUCB - A contextual multi-armed bandit approach

Metadata Tag: Contextual Bandit, Thompson sampling, LinUCB, Multi-Armed Bandit Link: towardsdatascience.com/recommender-systems-using-linucb-a-contextual-multi-armed-bandit-appr...

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays B) Introduction Thompson sampling is an old heuristic that has a...

Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation

Related B) References www.chenshouyuan.com/papers/sdm14.pdf .

Burst-induced Multi-Armed Bandit for Learning Recommendation

Burst-induced Multi-Armed Bandit for Learning Recommendation B) Abstract 해결하려는 문제: a non-stationary and context-free Multi-Armed Bandit problem, 유저나 아이템에 대한 어떠한 정보가 없는 경우 C)...

Top-K Contextual Bandits with Equity of Exposure

Top-K Contextual Bandits with Equity of Exposure Abstract Probability Ranking Principle 에 의하면 top-K items 을 greedy 하게 rank 하는것이 optimal 함 대신 Introduction This work...

A Contextual-Bandit Approach to Personalized News Article Recommendation

Abstract 사용자와 콘텐츠 정보를 활용한 개인화 웹 서비스 (광고, 뉴스 등) 를 제공하는 것은 다음과 같은 두 가지 이유로 어렵다.

Deep Bayesian Bandits Showdown - An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

Summary 이 논문의 main research question: how approximated model posteriors affect the performance of decision making via Thompson Sampling in contextual bandits.

Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem

Dueling Bandit Gradient Descent B) Related Online learning to rank for information retrieval Multileave Gradient Descent C) References.

Burstiness scale - A parsimonious model for characterizing random series of events

Burstiness Scale - A Parsimonious Model for Characterizing Random Series of Events Poisson point process 일반적인 random series of events (RSEs) 를 characterize 할 수 있는 방법인...

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