Francesco Locatello

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Intensive care units (ICU) are increasingly looking towards machine lear...
06/09/2021 ∙ by Hugo Yèche, et al. ∙ 0 ∙ share

read it
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-supervised representation learning has shown remarkable success in ...
06/08/2021 ∙ by Julius von Kugelgen, et al. ∙ 11 ∙ share

read it
Boosting Variational Inference With Locally Adaptive Step-Sizes

Variational Inference makes a trade-off between the capacity of the vari...
05/19/2021 ∙ by Gideon Dresdner, et al. ∙ 0 ∙ share

read it
Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...
02/22/2021 ∙ by Bernhard Schölkopf, et al. ∙ 44 ∙ share

read it
On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...
10/27/2020 ∙ by Andrea Dittadi, et al. ∙ 24 ∙ share

read it
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the unsupervised learning of disentangled representation...
10/27/2020 ∙ by Francesco Locatello, et al. ∙ 27 ∙ share

read it
A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...
07/28/2020 ∙ by Francesco Locatello, et al. ∙ 15 ∙ share

read it
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open...
06/14/2020 ∙ by Frederik Träuble, et al. ∙ 14 ∙ share

read it
Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by obs...
02/07/2020 ∙ by Francesco Locatello, et al. ∙ 20 ∙ share

read it
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Learning meaningful and compact representations with structurally disent...
06/07/2019 ∙ by Muhammad Waleed Gondal, et al. ∙ 4 ∙ share

read it
On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...
05/31/2019 ∙ by Francesco Locatello, et al. ∙ 6 ∙ share

read it
Are Disentangled Representations Helpful for Abstract Visual Reasoning?

A disentangled representation encodes information about the salient fact...
05/29/2019 ∙ by Sjoerd van Steenkiste, et al. ∙ 3 ∙ share

read it
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

We consider the problem of recovering a common latent source with indepe...
05/16/2019 ∙ by Luigi Gresele, et al. ∙ 9 ∙ share

read it
Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone proble...
05/03/2019 ∙ by Francesco Locatello, et al. ∙ 12 ∙ share

read it
Stochastic Conditional Gradient Method for Composite Convex Minimization

In this paper, we propose the first practical algorithm to minimize stoc...
01/29/2019 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...
11/29/2018 ∙ by Francesco Locatello, et al. ∙ 4 ∙ share

read it
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

Human professionals are often required to make decisions based on comple...
06/06/2018 ∙ by Vincent Fortuin, et al. ∙ 0 ∙ share

read it
Boosting Black Box Variational Inference

Approximating a probability density in a tractable manner is a central t...
06/06/2018 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it
Clustering Meets Implicit Generative Models

Clustering is a cornerstone of unsupervised learning which can be though...
04/30/2018 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it
Revisiting First-Order Convex Optimization Over Linear Spaces

Two popular examples of first-order optimization methods over linear spa...
03/26/2018 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it
Boosting Variational Inference: an Optimization Perspective

Variational Inference is a popular technique to approximate a possibly i...
08/05/2017 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolf...
05/31/2017 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe

Two of the most fundamental prototypes of greedy optimization are the ma...
02/21/2017 ∙ by Francesco Locatello, et al. ∙ 0 ∙ share

read it

Francesco Locatello

Featured Co-authors

Yoshua Bengio

Bernhard Schölkopf

Volkan Cevher

Martin Jaggi

Jürgen Schmidhuber

Joydeep Ghosh

Anirudh Goyal

Stefan Bauer

Sylvain Gelly

Tom Rainforth

Gunnar Rätsch

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Boosting Variational Inference With Locally Adaptive Step-Sizes

Towards Causal Representation Learning

On the Transfer of Disentangled Representations in Realistic Settings

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

A Commentary on the Unsupervised Learning of Disentangled Representations

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Weakly-Supervised Disentanglement Without Compromises

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

On the Fairness of Disentangled Representations

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

Disentangling Factors of Variation Using Few Labels

Stochastic Conditional Gradient Method for Composite Convex Minimization

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series

Boosting Black Box Variational Inference

Clustering Meets Implicit Generative Models

Revisiting First-Order Convex Optimization Over Linear Spaces

Boosting Variational Inference: an Optimization Perspective

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe