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New algorithm achieves optimal regret in online inverse optimization

By PulseAugur Editorial · [1 sources] ·

A new deterministic algorithm has been developed that achieves optimal regret in online inverse linear optimization, running in polynomial time. This algorithm is a variation of existing variable-metric methods, incorporating a novel approach where metric updates are revoked if the query point moves too far from the update location. The research aims to efficiently learn an unknown linear objective function without direct observation, building upon previous work that achieved optimal regret but required a significantly higher computational cost. AI

IMPACT This research could lead to more efficient AI systems capable of learning complex objectives from limited feedback.

RANK_REASON Academic paper detailing a new algorithm for online inverse optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New algorithm achieves optimal regret in online inverse optimization

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Academic paper detailing a new algorithm for online inverse optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anupam Gupta, Guru Guruganesh, Honghao Lin, Vahab Mirrokni, Renato Paes Leme, David P. Woodruff ·

    Optimal and Efficient Online Inverse Optimization

    arXiv:2610.08735v1 Announce Type: new Abstract: In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear objective on $\mathbb{R}^{d}$; the goal is to learn to optimize this objective wit…

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