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New framework tackles learning with adversarial preference feedback in MDPs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for learning in state-wise preference-based Markov Decision Processes (MDPs). This approach addresses scenarios where the comparison of two options not only reveals a preference but also influences the subsequent state, such as in recommendation systems. The paper analyzes the minimax regret for this problem, showing it is influenced by factors like episode length, number of states, and arms, and proposes algorithms to achieve optimal regret rates. AI

IMPACT Introduces a novel theoretical framework for learning with preference feedback in sequential decision-making scenarios.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and algorithms for a specific type of machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework tackles learning with adversarial preference feedback in MDPs

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The cluster contains an academic paper detailing a new theoretical framework and algorithms for a specific type of machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Taira Tsuchiya, Shinji Ito, Haipeng Luo ·

    From Dueling Bandits to Episodic MDPs: Learning with Adversarial Preference Feedback

    arXiv:2507.11706v2 Announce Type: replace-cross Abstract: In dueling bandits, the learner compares two arms in each round and observes only which one is preferred. This paper asks what changes when the compared pair also affects the next state, as in a recommendation interface wh…

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