Author

Guru Guruganesh

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#machine learning Preprint Oct 2026

Optimal and Efficient Online Inverse Optimization

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 without observing it. Sakaue recently obtained the optimal regret $O(\sqrt d)$ with a rando...

Anupam Gupta, Guru Guruganesh, Hong-Hao Lin et al. · 0 citations

#machine learning Preprint Sep 2026

Efficient Online Inverse Optimization with $O(d)$ Regret

This work builds on the variable-metric framework of Sakaue et al., adding a self-normalized rank-one update, and replaces the $\log\det$ potential by the trace power $\tr(H^{-1/2})$, which is bounded outright and removes the $\ln T$.

Yang Cai, Anupam Gupta, Vineet Gupta et al. · 3 citations · ⚡1