Author

Yanjun Han

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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