Francesco Locatello

发表

On the Fairness of Disentangled Representations

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

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

Martin Jaggi, Francesco Locatello, Rajiv Khanna, 2017, AISTATS.

On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset

Bernhard Schölkopf, Francesco Locatello, Olivier Bachem, 2019, NeurIPS 2019.

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

Francesco Locatello, Olivier Bachem, Mario Lucic, 2020, J. Mach. Learn. Res..

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA pdf

Bernhard Schölkopf, Luigi Gresele, Francesco Locatello, 2019, UAI.

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Gunnar Rätsch, Martin Jaggi, Francesco Locatello, 2017, NIPS.

SCIM: universal single-cell matching with unpaired feature sets

Gunnar Rätsch, Francesco Locatello, Franziska Singer, 2020, bioRxiv.

Boosting Black Box Variational Inference

Gunnar Rätsch, Francesco Locatello, Rajiv Khanna, 2018, NeurIPS.

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

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

Stochastic Frank-Wolfe for Composite Convex Minimization

Alp Yurtsever, Francesco Locatello, Olivier Fercoq, 2019, NeurIPS.

On Matching Pursuit and Coordinate Descent

Gunnar Rätsch, Bernhard Schölkopf, Martin Jaggi, 2018, ICML 2018.

Weakly-Supervised Disentanglement Without Compromises pdf

Francesco Locatello, Olivier Bachem, Michael Tschannen, 2020, ICML.

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

Stochastic Conditional Gradient Method for Composite Convex Minimization pdf

Volkan Cevher, Alp Yurtsever, Francesco Locatello, 2019, ArXiv.

Disentangling Factors of Variations Using Few Labels pdf

Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, 2020, ICLR.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series pdf

Gunnar Rätsch, Francesco Locatello, Vincent Fortuin, 2019, ICLR.

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Volkan Cevher, Alp Yurtsever, Francesco Locatello, 2018, ICML.

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

Sjoerd van Steenkiste, Jurgen Schmidhuber, Francesco Locatello, 2019, NeurIPS.

A Commentary on the Unsupervised Learning of Disentangled Representations pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2020, AAAI.

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization pdf

Laurent El Ghaoui, Francesco Locatello, Fabian Pedregosa, 2020, ICML.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Gunnar Rätsch, Francesco Locatello, Matthias Hüser, 2018, ICLR 2018.

Clustering Meets Implicit Generative Models pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018, ICLR.

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

Francesco Locatello, Bernhard Scholkopf, Niki Kilbertus, 2020, ArXiv.

Boosting Variational Inference: an Optimization Perspective

Gunnar Rätsch, Joydeep Ghosh, Francesco Locatello, 2017, AISTATS.

On Matching Pursuit and Coordinate Descent

Gunnar Rätsch, Bernhard Schölkopf, Martin Jaggi, 2018, ICML.

Object-Centric Learning with Slot Attention pdf

Georg Heigold, Alexey Dosovitskiy, Jakob Uszkoreit, 2020, NeurIPS.

On the Transfer of Disentangled Representations in Realistic Settings pdf

Ole Winther, Francesco Locatello, Bernhard Scholkopf, 2020, ArXiv.

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations pdf

Bernhard Schölkopf, Francesco Locatello, Olivier Bachem, 2018, ICML.

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Francesco Locatello, Matthias Huser, Gideon Dresdner, 2021, ICML.

Towards Causal Representation Learning

Yoshua Bengio, Francesco Locatello, Anirudh Goyal, 2021, ArXiv.

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

Luigi Gresele, Francesco Locatello, Wieland Brendel, 2021, NeurIPS.

Boosting Variational Inference With Locally Adaptive Step-Sizes pdf

Francesco Locatello, Fabian Pedregosa, Gideon Dresdner, 2021, IJCAI.

On Disentangled Representations Learned from Correlated Data

Francesco Locatello, Anirudh Goyal, Bernhard Scholkopf, 2020, International Conference on Machine Learning.