Introduction to online convex optimization
E Hazan - Foundations and Trends® in Optimization, 2016 - nowpublishers.com
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 …
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …
A survey on distributed online optimization and online games
Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …
Revisiting Frank-Wolfe: Projection-free sparse convex optimization
M Jaggi - International conference on machine learning, 2013 - proceedings.mlr.press
We provide stronger and more general primal-dual convergence results for Frank-Wolfe-
type algorithms (aka conditional gradient) for constrained convex optimization, enabled by a …
type algorithms (aka conditional gradient) for constrained convex optimization, enabled by a …
Conditional gradient methods
G Braun, A Carderera, CW Combettes… - arxiv preprint arxiv …, 2022 - arxiv.org
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 …
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …
Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
We show that many machine learning goals can be expressed as “rate constraints” on a
model's predictions. We study the problem of training non-convex models subject to these …
model's predictions. We study the problem of training non-convex models subject to these …
Reinforcement and imitation learning via interactive no-regret learning
S Ross, JA Bagnell - arxiv preprint arxiv:1406.5979, 2014 - arxiv.org
Recent work has demonstrated that problems--particularly imitation learning and structured
prediction--where a learner's predictions influence the input-distribution it is tested on can be …
prediction--where a learner's predictions influence the input-distribution it is tested on can be …
Understanding alternating minimization for matrix completion
M Hardt - 2014 IEEE 55th Annual Symposium on Foundations …, 2014 - ieeexplore.ieee.org
Alternating minimization is a widely used and empirically successful heuristic for matrix
completion and related low-rank optimization problems. Theoretical guarantees for …
completion and related low-rank optimization problems. Theoretical guarantees for …
Variance-reduced and projection-free stochastic optimization
Abstract The Frank-Wolfe optimization algorithm has recently regained popularity for
machine learning applications due to its projection-free property and its ability to handle …
machine learning applications due to its projection-free property and its ability to handle …
Nearly optimal private lasso
We present a nearly optimal differentially private version of the well known LASSO estimator.
Our algorithm provides privacy protection with respect to each training data item. The excess …
Our algorithm provides privacy protection with respect to each training data item. The excess …
Optimization with first-order surrogate functions
J Mairal - International Conference on Machine Learning, 2013 - proceedings.mlr.press
In this paper, we study optimization methods consisting of iteratively minimizing surrogates
of an objective function. By proposing several algorithmic variants and simple convergence …
of an objective function. By proposing several algorithmic variants and simple convergence …