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Tag: contextual

  • Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach

    Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach arXiv:2601.11016v1 Announce Type: new Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates.…

  • Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions

    Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions arXiv:2512.06270v1 Announce Type: new Abstract: In this work, we study contextual strongly convex simulation optimization and adopt an “optimize then predict” (OTP) approach for real-time decision making. In the offline stage, simulation optimization is conducted across a set of covariates to approximate the optimal-solution…

  • Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation

    Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation arXiv:2509.05852v1 Announce Type: new Abstract: Motivated by the need for rigorous and scalable evaluation of large language models, we study contextual preference inference for pairwise comparison functionals of context-dependent preference score functions across domains. Focusing on the contextual Bradley-Terry-Luce model, we develop…

  • Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates

    Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates arXiv:2503.16941v1 Announce Type: new Abstract: Personalized services are central to today’s digital landscape, where online decision-making is commonly formulated as contextual bandit problems. Two key challenges emerge in modern applications: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate…

  • Sparse Nonparametric Contextual Bandits

    Sparse Nonparametric Contextual Bandits arXiv:2503.16382v1 Announce Type: new Abstract: This paper studies the problem of simultaneously learning relevant features and minimising regret in contextual bandit problems. We introduce and analyse a new class of contextual bandit problems, called sparse nonparametric contextual bandits, in which the expected reward function lies in the linear span of a…

  • Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback

    Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback arXiv:2412.19436v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback, driven by diverse individual contexts and preferences, poses significant challenges for reward learning. To address this, we propose…