Breaking the $T^{3/4}$ Barrier for Regret Minimization With Bi-Dimensional CDFs

Abstract

We study regret minimization for learning CDF-related objectives of the form \[ g(x)\cdot\mathbb{P}_{X\sim\mathcal{D}}(X\le x), \] over $[0,1{]}^2$, where $g$ is a known Lipschitz function and $\mathcal{D}$ is an unknown distribution. At each round $t$, the learner selects a point $x_t$ and observes the binary feedback $\mathbb{I}(X_t\le x_t)$, where $X_t\sim \mathcal{D}$. We design an algorithm achieving regret $\tilde{\mathcal{O}}(T^{7/10})$, improving over the previous best-known bound of $\tilde{\mathcal{O}}(T^{3/4})$ and showing that the curse of dimensionality can be at least partially lifted for this class of objectives, though a gap remains with the $\Omega (T^{2/3})$ lower bound. As an application, our techniques yield the same $\tilde{\mathcal{O}}(T^{7/10})$ regret bound for profit maximization in repeated bilateral trade with fixed prices.