The purpose of this survey is to serve both as a gentle introduction and a coherent overview
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …

CLIP proved that aligning visual and language spaces is key to solving many vision tasks
without explicit training, but required to train image and text encoders from scratch on a huge …

We survey optimization problems that involve the cardinality of variable vectors in
constraints or the objective function. We provide a unified viewpoint on the general problem …

In modern supervised learning, many deep neural networks are able to interpolate the data:
the empirical loss can be driven to near zero on all samples simultaneously. In this work, we …

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 …

Abstract Structured constraints in Machine Learning have recently brought the Frank-Wolfe
(FW) family of algorithms back in the spotlight. While the classical FW algorithm has poor …

Gaussian processes (GPs) are non-parametric probabilistic regression models that are
popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates …

We propose a novel Stochastic Frank-Wolfe (aka conditional gradient) algorithm for
constrained smooth finite-sum minimization with a generalized linear prediction/structure …

We propose a conditional gradient framework for a composite convex minimization template
with broad applications. Our approach combines smoothing and homotopy techniques …

Abstract Conditional Gradients (aka Frank-Wolfe algorithms) form a classical set of methods
for constrained smooth convex minimization due to their simplicity, the absence of projection …