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Related publications (27)

Planted XY model: Thermodynamics and inference

Lenka Zdeborová, Guanhao Huang, Siyu Chen, Giovanni Piccioli

In this paper we study a fully connected planted spin glass named the planted XY model. Motivation for studying this system comes both from the spin glass field and the one of statistical inference where it models the angular synchronization problem. We de ...

2022

Generalized approximate survey propagation for high-dimensional estimation

Yue Lu, Luca Saglietti

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 Universality of Low-Density Parity-Check Block Codes

Rüdiger Urbanke, Wei Liu

An important class of modern channel codes is the capacity-achieving sequences of low-density parity-check block codes. Such sequences are usually designed for the binary erasure channel and are decoded by iterative message-passing algorithms. In this pape ...

IEEE2020

Deep Unfolding for Communications Systems: A Survey and Some New Directions

Alexios Konstantinos Balatsoukas Stimming, Christoph Studer

Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey sum ...

IEEE2019

Subadditivity Beyond Trees and the Chi-Squared Mutual Information

Emmanuel Abbé

Evans et al. [1] proved the subadditivity of the mutual information in the broadcasting on tree model with binary vertex labels and symmetric edge channels. They raised the question of whether such subadditivity extends to loopy graphs in some appropriate ...

IEEE2019

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Volkan Cevher, Paul Thierry Yves Rolland, Jonathan Mark Scarlett, Ilija Bogunovic

Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of a ...

2018

A Compressed Sensing Approach for Distribution Matching

Vahid Aref, Mohamad Baker Dia

In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary so ...

IEEE2018

ScienceWISE: Topic Modeling over Scientific Literature Networks

Paolo De Los Rios, Alessio Vincenzo Cardillo, Andrea Martini

We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of te ...

2017

Reconstruction using approximate message passing methods

Paul Hurley, Matthieu Martin Jean-André Simeoni

The present invention is notably directed to a computer-implemented method for image reconstruction. The method comprises: accessing elements that respectively correspond to measurement values, which can be respectively mapped to measurement nodes; and per ...

2017

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

Ulugbek Kamilov

Generalized linear models, where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = A x, arise in a range of applications in nonlinear filtering and regression. Approximate message passing (AMP) methods, b ...

IEEE2017

Scampi: a robust approximate message-passing framework for compressive imaging

Florent Gérard Krzakala, Jean François Emmanuel Barbier

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices

Volkan Cevher

The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map arises in a range of applications. Approximate message passing (AMP) methods, based on Gaussian appr ...

2016

Learning Search Strategies from Human Demonstrations

Guillaume Pierre Luc De Chambrier

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016

Asynchronous Decoding of LDPC Codes over BEC

Amin Karbasi, Amir Hesam Salavati, Saeid Haghighatshoar

LDPC codes are typically decoded by running a synchronous message passing algorithm over the corresponding bipartite factor graph (made of variable and check nodes). More specifically, each synchronous round consists of 1) updating all variable nodes based ...

2015

Bilinear Generalized Approximate Message Passing—Part II: Applications

Volkan Cevher

In this paper, we extend the generalized approximate message passing (G-AMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of compressive sensing, to the generalized-bilinear case. In Part I of this two-par ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Bilinear Generalized Approximate Message Passing—Part I: Derivation

Volkan Cevher

Institute of Electrical and Electronics Engineers2014

Threshold Saturation for Spatially Coupled LDPC and LDGM Codes on BMS Channels

Nicolas Macris

Spatially-coupled low-density parity-check (LDPC) codes, which were first introduced as LDPC convolutional codes, have been shown to exhibit excellent performance under low-complexity belief-propagation decoding. This phenomenon is now termed threshold sat ...

Institute of Electrical and Electronics Engineers2014

Message-Passing Algorithms: Reparameterizations and Splittings

Nicholas Ruozzi

The max-product algorithm, a local message-passing scheme that attempts to compute the most probable assignment (MAP) of a given probability distribution, has been successfully employed as a method of approximate inference for applications arising in codin ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Rüdiger Urbanke, Shrinivas Kudekar

We investigate spatially coupled code ensembles. For transmission over the binary erasure channel, it was recently shown that spatial coupling increases the belief propagation threshold of the ensemble to essentially the maximum a priori threshold of the u ...

Institute of Electrical and Electronics Engineers2013

Convergent Message-Passing Algorithms in the Presence of Erasures

Nicholas Ruozzi

We examine how to design convergent and correct message-passing schemes, similar to the min-sum algorithm, for maximum a posteriori (MAP) estimation in the case that the messages passed between two nodes of the network may never be delivered. The proposed ...

Ieee2013