# Improving Offline Contextual Bandits with Distributional Robustness

arXiv: https://arxiv.org/abs/2011.06835 · HTML: https://mlsanity.com/paper/2011.06835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [PAC-Bayesian Offline Contextual Bandits With Guarantees](https://mlsanity.com/paper/2210.13132/) (2022)
- [Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning](https://mlsanity.com/paper/2405.14335/) (2024)
- [Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization](https://mlsanity.com/paper/2306.09222/) (2023)
- [Fast Offline Policy Optimization for Large Scale Recommendation](https://mlsanity.com/paper/2208.05327/) (2022)
- [Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms](https://mlsanity.com/paper/2508.02812/) (2025)

## Abstract (arXiv)

This paper extends the Distributionally Robust Optimization (DRO) approach for offline contextual bandits. Specifically, we leverage this framework to introduce a convex reformulation of the Counterfactual Risk Minimization principle. Besides relying on convex programs, our approach is compatible with stochastic optimization, and can therefore be readily adapted tothe large data regime. Our approach relies on the construction of asymptotic confidence intervals for offline contextual bandits through the DRO framework. By leveraging known asymptotic results of robust estimators, we also show how to automatically calibrate such confidence intervals, which in turn removes the burden of hyper-parameter selection for policy optimization. We present preliminary empirical results supporting the effectiveness of our approach.