Low-rank modeling plays a pivotal role in signal processing and machine learning, with
applications ranging from collaborative filtering, video surveillance, and medical imaging to …

Fine-tuning is the primary methodology for tailoring pre-trained large language models to
specific tasks. As the model's scale and the diversity of tasks expand, parameter-efficient fine …

This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

Conditional gradient methods (CGM) are widely used in modern machine learning. CGM's
overall running time usually consists of two parts: the number of iterations and the cost of …

In many online learning problems the computational bottleneck for gradient-based methods
is the projection operation. For this reason, in many problems the most efficient algorithms …

Abstract The Frank-Wolfe method solves smooth constrained convex optimization problems
at a generic sublinear rate of $\mathcal {O}(1/T) $, and it (or its variants) enjoys accelerated …

The Euclidean projection onto a convex set is an important problem that arises in numerous
constrained optimization tasks. Unfortunately, in many cases, computing projections is …

We propose a simple modification to the iterative hard thresholding (IHT) algorithm, which
recovers asymptotically sparser solutions as a function of the condition number. When …

White box adversarial perturbations are generated via iterative optimization algorithms most
often by minimizing an adversarial loss on a ℓ p neighborhood of the original image, the so …

We revisit the challenge of designing online algorithms for the bandit convex optimization
problem (BCO) which are also scalable to high dimensional problems. Hence, we consider …