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Related publications (37)

Tree-AMP: Compositional Inference with Tree Approximate Message Passing

Florent Gérard Krzakala, Lenka Zdeborová

We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms ...

2023

Planted XY model: Thermodynamics and inference

Lenka Zdeborová, Guanhao Huang, Giovanni Piccioli, Siyu Chen

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 d ...

2022

The Stability of Low-Density Parity-Check Codes and Some of its Consequences

Rüdiger Urbanke, Wei Liu

We study the stability of low-density parity-check codes under blockwise or bitwise maximum a posteriori decoding, where transmission takes place over a binary-input memoryless output-symmetric channel. Our study stems from the consideration of constructin ...

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

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, Enric Boix Adsera

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

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