Publications related to Laboratory for Information and Inference Systems

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

2021

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 in the context of discrete-timeMarkov chainMonteCarlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so ...

SPRINGER2021

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Karl Aberer, Rémi Philippe Lebret, Mohammadreza Banaei

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression of thes ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Soham Sarkar

Testing mutual independence among several random vectors of arbitrary dimensions is a challenging problem in Statistics, and it has gained considerable interest in recent years. In this article, we propose some nonparametric tests based on different notion ...

TAYLOR & FRANCIS LTD2021

Deep visual Re-identification with confidence

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand th ...

2021

A unifying framework for form-finding and topology-finding of tensegrity structures

Yafeng Wang

This paper presents a unifying framework for the form-finding and topology-finding of tensegrity structures. The novel computational framework is based on rank-constrained linear matrix inequalities. For form-finding, given the topology (i.e., member conne ...

PERGAMON-ELSEVIER SCIENCE LTD2021

A fast spectral divide-and-conquer method for banded matrices

Daniel Kressner, Ana Susnjara

Based on the spectral divide-and-conquer algorithm by Nakatsukasa and Higham [SIAM J. Sci. Comput., 35(3):A1325-A1349, 2013], we propose a new algorithm for computing all the eigenvalues and eigenvectors of a symmetric banded matrix with small bandwidth, w ...

WILEY2021

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

Matthieu Wyart, Francesco Cagnetta, Alessandro Favero

Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher-student framework for kernel r ...

2021

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Patrick Thiran, Farnood Salehi, Mahsa Forouzesh

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the lo ...

IEEE2021

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

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Addressing fairness in classification with a model-agnostic multi-objective algorithm

Generalized parallel tempering on Bayesian inverse problems

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Deep visual Re-identification with confidence

A unifying framework for form-finding and topology-finding of tensegrity structures

A fast spectral divide-and-conquer method for banded matrices

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Convergence of adaptive algorithms for constrained weakly convex optimization

Generalized parallel tempering on Bayesian inverse problems

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Deep visual Re-identification with confidence

A unifying framework for form-finding and topology-finding of tensegrity structures

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Convergence of adaptive algorithms for constrained weakly convex optimization

A fast spectral divide-and-conquer method for banded matrices