Matteo Castiglioni

Achieving a regret bound of \(\widetilde{\mathcal{O}}(T^{7/10})\) breaks the previous barrier and challenges assumptions about the curse of dimensionality in regret minimization.

Computing approximate stationary points in min-max optimization for quadratic polynomials is proven to be PPAD-hard, revealing deep implications for game theory and optimization.

Finally, a constrained MAB algorithm that gracefully degrades under adversarial constraint drift, achieving near-optimal regret when constraints are stochastic and smoothly transitioning to adversarial robustness.

Replicable algorithms can now achieve the same performance as non-replicable ones in constrained multi-armed bandit problems, opening the door to more reliable and reproducible online learning experiments.