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[BOK][B] First-order and stochastic optimization methods for machine learning
G Lan - 2020 - Springer
Since its beginning, optimization has played a vital role in data science. The analysis and
solution methods for many statistical and machine learning models rely on optimization. The …
solution methods for many statistical and machine learning models rely on optimization. The …
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 …
One sample stochastic frank-wolfe
One of the beauties of the projected gradient descent method lies in its rather simple
mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its …
mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its …
Conditional gradient methods via stochastic path-integrated differential estimator
We propose a class of variance-reduced stochastic conditional gradient methods. By
adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of …
adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of …
Stochastic frank-wolfe: Unified analysis and zoo of special cases
Abstract The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known
methods for solving constrained optimization problems appearing in various machine …
methods for solving constrained optimization problems appearing in various machine …
Stochastic conditional gradient++:(non) convex minimization and continuous submodular maximization
In this paper, we consider the general nonoblivious stochastic optimization where the
underlying stochasticity may change during the optimization procedure and depends on the …
underlying stochasticity may change during the optimization procedure and depends on the …
Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks
In this paper, we consider the eigenvalue PDE problem of the infinitesimal generators of
metastable diffusion processes. We propose a numerical algorithm based on training …
metastable diffusion processes. We propose a numerical algorithm based on training …
Zeroth-order methods for constrained nonconvex nonsmooth stochastic optimization
This paper studies the problem of solving nonconvex nonsmooth optimization over a closed
convex set. Most previous works tackle such problems by transforming the constrained …
convex set. Most previous works tackle such problems by transforming the constrained …
Constrained stochastic nonconvex optimization with state-dependent markov data
We study stochastic optimization algorithms for constrained nonconvex stochastic
optimization problems with Markovian data. In particular, we focus on the case when the …
optimization problems with Markovian data. In particular, we focus on the case when the …
Accelerated stochastic gradient-free and projection-free methods
In the paper, we propose a class of accelerated stochastic gradient-free and projection-free
(aka, zeroth-order Frank-Wolfe) methods to solve the constrained stochastic and finite-sum …
(aka, zeroth-order Frank-Wolfe) methods to solve the constrained stochastic and finite-sum …