# Practical Contextual Bandits with Regression Oracles

## TL;DR

This paper introduces a new practical and theoretically sound approach for contextual bandits that uses regression oracles, generalizes existing methods, and performs well empirically.

## Contribution

It proposes a novel technique combining realizability and agnostic methods using regression oracles, extending UCB and LinUCB to more expressive models.

## Key findings

- Achieves low regret under certain assumptions
- Performs comparably or better than existing baselines
- Generalizes UCB and LinUCB to complex models

## Abstract

A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded. We present a new technique that has the empirical and computational advantages of realizability-based approaches combined with the flexibility of agnostic methods. Our algorithms leverage the availability of a regression oracle for the value-function class, a more realistic and reasonable oracle than the classification oracles over policies typically assumed by agnostic methods. Our approach generalizes both UCB and LinUCB to far more expressive possible model classes and achieves low regret under certain distributional assumptions. In an extensive empirical evaluation, compared to both realizability-based and agnostic baselines, we find that our approach typically gives comparable or superior results.