# Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

arXiv: https://arxiv.org/abs/2605.01752 · HTML: https://mlsanity.com/paper/2605.01752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 20 of 20)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Contextual Dueling Bandits](https://mlsanity.com/paper/1502.06362/) (2015)
- [Better Algorithms for Stochastic Bandits with Adversarial Corruptions](https://mlsanity.com/paper/1902.08647/) (2019)
- [Linear Bandits with Stochastic Delayed Feedback](https://mlsanity.com/paper/1807.02089/) (2018)
- [Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2205.06811/) (2022)
- [Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models](https://mlsanity.com/paper/2202.04593/) (2022)
- [Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes](https://mlsanity.com/paper/2212.05949/) (2022)
- [Feel-Good Thompson Sampling for Contextual Dueling Bandits](https://mlsanity.com/paper/2404.06013/) (2024)
- [Delayed Feedback in Generalised Linear Bandits Revisited](https://mlsanity.com/paper/2207.10786/) (2022)
- [Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits](https://mlsanity.com/paper/2310.00968/) (2023)
- [A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs](https://mlsanity.com/paper/2305.08629/) (2023)
- [Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2110.12175/) (2021)
- [Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback](https://mlsanity.com/paper/2404.10776/) (2024)
- [Robust Bandit Learning with Imperfect Context](https://mlsanity.com/paper/2102.05018/) (2021)
- [Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification](https://mlsanity.com/paper/2410.07533/) (2024)
- [Robust Contextual Linear Bandits](https://mlsanity.com/paper/2210.14483/) (2022)
- [Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts](https://mlsanity.com/paper/2309.13896/) (2023)
- [Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration](https://mlsanity.com/paper/2506.01250/) (2025)

## Abstract (arXiv)

We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adversarial corruption. Feedback is subject to unknown stochastic or adversarial delays and a cumulative corruption budget C. To address these challenges, we propose e RCDP-UCB, which integrates a learned approximator that predicts post-serving contexts from pre-serving information. It further employs an adaptive weighting strategy that clips feature vectors to mitigate the impact of corrupted and delayed observations simultaneously. Under standard regularity conditions and a parametric post-serving mapping, we rigorously establish that our algorithm is delay-regime-agnostic, achieving a regret upper bound of widetildeO(d(√T + C + D)), where d is the total feature dimension and D encapsulates the delay complexity. Crucially, our analysis reveals an additive cost structure between corruption and delay, avoiding the multiplicative degradation typical of prior works. We further establish lower bounds that nearly match our upper bounds up to a √d factor for adversarial delays in the absence of post-serving contexts. Code is available at https://github.com/youngmin0oh/rcdp-public.