As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

Bandit problems with linear or concave reward have been extensively studied, but relatively
few works have studied bandits with non-concave reward. This work considers a large family …

In this work, we consider the optimization formulation for symmetric tensor decomposition
recently introduced in the Subspace Power Method (SPM) of Kileel and Pereira. Unlike …

This paper develops further the idea of perturbed gradient descent, by adapting perturbation
with the history of state via the notation of occupation time for saddle points. The proposed …

The advent of artificial intelligence (AI) has revolutionized a diverse set of complex decision-
making tasks in society and industry. Empirical Risk Minimization (ERM), as the dominant …

In today's rapidly evolving technological landscape, the development and advancement of
computational tools and algorithms have become paramount across a wide range of …

Motivated by the super-diffusivity of self-repelling random walk, which has roots in statistical
physics, this paper develops a new perturbation mechanism for optimization algorithms. In …

Motivated by the super-diffusivity of self-repelling random walk, which has roots in statistical
physics, this paper develops a new perturbation mechanism for optimization algorithms. In …