Publications associées à Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

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A first-order primal-dual method with adaptivity to local smoothness

Sublinear Algorithms for Spectral Graph Clustering

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Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems

Sublinear Algorithms for Spectral Graph Clustering

Automated Design: A Journey Across Modelling, Optimization, and Education

A first-order primal-dual method with adaptivity to local smoothness

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

On the advantages of P2P ML on mobile devices

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees