2,006 research outputs found
A Contextual Bandit Bake-off
Contextual bandit algorithms are essential for solving many real-world
interactive machine learning problems. Despite multiple recent successes on
statistically and computationally efficient methods, the practical behavior of
these algorithms is still poorly understood. We leverage the availability of
large numbers of supervised learning datasets to empirically evaluate
contextual bandit algorithms, focusing on practical methods that learn by
relying on optimization oracles from supervised learning. We find that a recent
method (Foster et al., 2018) using optimism under uncertainty works the best
overall. A surprisingly close second is a simple greedy baseline that only
explores implicitly through the diversity of contexts, followed by a variant of
Online Cover (Agarwal et al., 2014) which tends to be more conservative but
robust to problem specification by design. Along the way, we also evaluate
various components of contextual bandit algorithm design such as loss
estimators. Overall, this is a thorough study and review of contextual bandit
methodology
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Decision-focused learning (DFL) is an emerging paradigm in machine learning
which trains a model to optimize decisions, integrating prediction and
optimization in an end-to-end system. This paradigm holds the promise to
revolutionize decision-making in many real-world applications which operate
under uncertainty, where the estimation of unknown parameters within these
decision models often becomes a substantial roadblock. This paper presents a
comprehensive review of DFL. It provides an in-depth analysis of the various
techniques devised to integrate machine learning and optimization models,
introduces a taxonomy of DFL methods distinguished by their unique
characteristics, and conducts an extensive empirical evaluation of these
methods proposing suitable benchmark dataset and tasks for DFL. Finally, the
study provides valuable insights into current and potential future avenues in
DFL research.Comment: Experimental Survey and Benchmarkin
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