Publications related to Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Alexandre Caboussat, Dimitrios Gourzoulidis

We consider a least-squares/relaxation finite element method for the numerical solution of the prescribed Jacobian equation. We look for its solution via a least-squares approach. We introduce a relaxation algorithm that decouples this least-squares proble ...

SPRINGER/PLENUM PUBLISHERS2022

An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers

Marcos Rubinstein, Hamidreza Karami

Partial discharge (PD) occurrence in power transformers can lead to irreparable damage to the power network. In this paper, the inverse filter (IF) method to localize PDs in power transformers is proposed. To the best of the authors’ knowledge, this is the ...

2022

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

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre, Loucas Pillaud-Vivien

Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...

2021

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Nicolas Macris, Jean François Emmanuel Barbier, Chun Lam Chan

Recently, a new adaptive path interpolation method has been developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gen ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Generalized approximate survey propagation for high-dimensional estimation*

Yue Lu

In generalized linear estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy, channel. In the Bayesian optimal setting, generalized approximate message ...

IOP PUBLISHING LTD2020

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems

Fabio Nobile, Juan Pablo Madrigal Cianci

In the current work we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm. Such a method, found its origins in the molecular dynamics community, and can be understood as th ...

MATHICSE2020

The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference

Nicolas Macris, Jean François Emmanuel Barbier

In recent years important progress has been achieved towards proving the validity of the replica predictions for the (asymptotic) mutual information (or free energy) in Bayesian inference problems. The proof techniques that have emerged appear to be quite ...

SPRINGER HEIDELBERG2019

Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers

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

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Generalized approximate survey propagation for high-dimensional estimation*

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems

The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Generalized approximate survey propagation for high-dimensional estimation*

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

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems

An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers

The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model