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Fléau de la dimension

Publications associées (331)

Uncertainty-aware Model Inversion Networks

In this thesis, we assess a new framework called UMIN on a data-driven optimization problem. Such a problem happens recurrently in real life and can quickly become dicult to model when the input has a high dimensionality as images for instance. From the ar ...

2020

Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys

Anirudh Raju Natarajan

The free energy plays a fundamental role in theories of phase transformations and microstructure evolution. It encodes the thermodynamic coupling between different fields, such as mechanics and chemistry, within continuum descriptions of non-equilibrium ma ...

Elsevier2020

Carving out OPE space and precise O(2) model critical exponents

Alessandro Vichi, Ning Su

We develop new tools for isolating CFTs using the numerical bootstrap. A "cutting surface" algorithm for scanning OPE coefficients makes it possible to find islands in high-dimensional spaces. Together with recent progress in large-scale semidefinite progr ...

SPRINGER2020

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

On the optimal sampling design for the Graph Total Variation decoder: recovering piecewise-constant graph signals from a few vertex measurements

Rodrigo Cerqueira Gonzalez Pena

Compressed Sensing teaches us that measurements can be traded for offline computation if the signal being sensed has a simple enough representation. Proper decoders can exactly recover the high-dimensional signal of interest from a lower-dimensional vector ...

EPFL2020

The Spiked Matrix Model With Generative Priors

Florent Gérard Krzakala, Lenka Zdeborová, Bruno Loureiro, Bruno Loureiro

We investigate the statistical and algorithmic properties of random neural-network generative priors in a simple inference problem: spiked-matrix estimation. We establish a rigorous expression for the performance of the Bayes-optimal estimator in the high- ...

2020

Conditional gradient methods for stochastically constrained convex minimization

Volkan Cevher, Ya-Ping Hsieh, Ahmet Alacaoglu, Maria-Luiza Vladarean

We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints. Instances of this template naturally arise from SDP-relaxations of combinatorial problems, whi ...

2020

Genuinely Distributed Byzantine Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Association for Computing Machinery2020

Adaptation and Learning Over Networks Under Subspace Constraints-Part I: Stability Analysis

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Florent Gérard Krzakala, Lenka Zdeborová

Despite the widespread use of gradient-based algorithms for optimizing high-dimensional non-convex functions, understanding their ability of finding good minima instead of being trapped in spurious ones remains to a large extent an open problem. Here we fo ...

Curran Associates, Inc.2020

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