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  • Contextual Linear Optimization with Bandit Feedback
    We study a class of algorithms for CLO with bandit feedback, which we term induced empirical risk minimization (IERM), where we fit a predictive model to directly optimize the downstream performance of the policy it induces
  • Contextual Linear Optimization with Bandit Feedback | alphaXiv
    This research extends Contextual Linear Optimization (CLO) to the challenging bandit feedback setting, where only the cost of the chosen decision is observed, by adapting the Induced Empirical Risk Minimization (IERM) framework
  • Contextual Linear Optimization with Partial Feedback - ADS
    We propose a unified class of offline learning algorithms for CLO with different types of feedback, following a powerful induced empirical risk minimization (IERM) framework that integrates estimation and optimization
  • Lecture 7 Learning in games: Bandit feedback
    cision maker only receives partial feedback In this lecture, we consider the case where the decision maker receives feedb ck only on the utility of the chosen action
  • Contextual Linear Optimization with Bandit Feedback
    Abstract summary: Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients We study a class of offline learning algorithms for CLO with bandit feedback
  • Contextual Linear Optimization with Bandit Feedback
    We study a class of offline learning algorithms for CLO with bandit feedback, which we term induced empirical risk minimization (IERM), where we fit a predictive model to directly optimize the downstream performance of the policy it induces
  • Contextual Linear Optimization with Bandit Feedback
    This paper is crucial because it bridges the gap between contextual linear optimization (CLO) theory and practical application by addressing the realistic scenario of bandit feedback
  • Contextual Linear Optimization with Bandit Feedback
    i We term this observation model bandit feedback as it corresponds to observing the cost only of a given decision and not the counterfactual cost of any alternative decision, as in bandit problems [25]
  • Contextual Linear Optimization with Bandit Feedback
    In this section, we provide a theoretical regret analysis for the IERM approach with bandit feedback, allowing for model misspecification in the induced policy class ΠF
  • 1 Introduction - arXiv. org
    We study a class of algorithms for CLO with bandit feedback, which we term induced empirical risk minimization (IERM), where we fit a predictive model to directly optimize downstream performance of the policy it induces





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