# Semiparametric Contextual Bandits

arXiv: https://arxiv.org/abs/1803.04204 · HTML: https://mlsanity.com/paper/1803.04204/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 19 of 19)

- [Bias-Robust Bayesian Optimization via Dueling Bandits](https://mlsanity.com/paper/2105.11802/) (2021)
- [A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits](https://mlsanity.com/paper/2202.01850/) (2022)
- [Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model](https://mlsanity.com/paper/1901.11221/) (2019)
- [Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk](https://mlsanity.com/paper/2304.04341/) (2023)
- [Model Selection for Generic Contextual Bandits](https://mlsanity.com/paper/2107.03455/) (2021)
- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions](https://mlsanity.com/paper/2312.06403/) (2023)
- [Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization](https://mlsanity.com/paper/2205.08295/) (2022)
- [A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations](https://mlsanity.com/paper/2303.11187/) (2023)
- [Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles](https://mlsanity.com/paper/2203.16668/) (2022)
- [GBOSE: Generalized Bandit Orthogonalized Semiparametric Estimation](https://mlsanity.com/paper/2301.08781/) (2023)
- [Active and Passive Causal Inference Learning](https://mlsanity.com/paper/2308.09248/) (2023)
- [Experimental Design for Semiparametric Bandits](https://mlsanity.com/paper/2506.13390/) (2025)
- [Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach](https://mlsanity.com/paper/2209.05186/) (2022)
- [Selective Uncertainty Propagation in Offline RL](https://mlsanity.com/paper/2302.00284/) (2023)
- [Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics](https://mlsanity.com/paper/2305.18333/) (2023)
- [Zero-Inflated Bandits](https://mlsanity.com/paper/2312.15595/) (2023)
- [Nearly Optimal Best Arm Identification for Semiparametric Bandits](https://mlsanity.com/paper/2604.03969/) (2026)
- [Odds-Ratio Thompson Sampling: A Specification and Design Guide for Contrast-Based Multi-Armed Bandits](https://mlsanity.com/paper/2609.19709/) (2026)

## Abstract (arXiv)

This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term. We design new algorithms that achieve tildeO(d√T) regret over T rounds, when the linear function is d-dimensional, which matches the best known bounds for the simpler unconfounded case and improves on a recent result of Greenewald et al. (2017). Via an empirical evaluation, we show that our algorithms outperform prior approaches when there are non-linear confounding effects on the rewards. Technically, our algorithms use a new reward estimator inspired by doubly-robust approaches and our proofs require new concentration inequalities for self-normalized martingales.