Author

Nived Rajaraman

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The Price of Hidden Curvature: An (cid:101) Ω ( 𝑑 5 / 4 √ 𝑇 ) Lower Bound for Bandit Convex Optimization

This work establishes a (cid:101) Ω ( 𝑑 5 / 4 √ 𝑇 ) lower bound on the minimax expected regret of stochastic bandit convex optimization of 1-Lipschitz functions on the Euclidean ball, establishing that stochastic bandit convex optimization is fundamentally harder than linear bandits.

Nived Rajaraman · 0 citations

Preprint Jul 2026

The Price of Hidden Curvature: Improved Lower Bounds for Bandit Convex Optimization

Improved lower bounds are established on the minimax expected regret of stochastic bandit convex optimization for $1$-Lipschitz functions on the $d$-dimensional Euclidean ball, showing that stochastic bandit convex optimization is fundamentally harder than linear bandits.

Nived Rajaraman, Yanjun Han · 1 citation