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bandit

bandit

12개의 글

  • A Contextual-Bandit Approach to Personalized News Article Recommendation

    • MAB
    • bandit
    • linear_regression
    • paper_review
    • recommendation_system
  • An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits

    • MAB
    • NIPS
    • bandit
    • contextual_bandit
    • linear_regression
    • paper_review
    • y2020
  • Burst-induced Multi-Armed Bandit for Learning Recommendation

    • MAB
    • RecSyS
    • bandit
    • paper_review
  • Burstiness scale - A parsimonious model for characterizing random series of events

    • KDD
    • bandit
    • paper_review
  • Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation

    • bandit
    • contextual_bandit
    • paper_review
  • Deep Bayesian Bandits - Exploring in Online Personalized Recommendations

    • bandit
    • paper_review
    • recommendation_system
    • exploration
    • y2020
  • Deep Bayesian Bandits Showdown - An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

    • Google
    • ICLR
    • MAB
    • bandit
    • deep_learning
    • paper_review
    • y2018
  • Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem

    • bandit
    • optimization
    • paper_review
  • Mortal Multi-Armed Bandits

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

    • MAB
    • bandit
    • paper_review
  • Recommender systems using LinUCB - A contextual multi-armed bandit approach

    • MAB
    • bandit
    • paper_review
    • recommendation_system
    • thompson_sampling
  • Top-K Contextual Bandits with Equity of Exposure

    • RecSyS
    • bandit
    • contextual_bandit
    • paper_review
    • y2021