Francesco Locatello
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
43
Venues
9
Active years
2017–2025
Best venue rank
A*
Where they publish
Papers
43 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Scalable Mechanistic Neural Networks. | Jiale Chen, Dingling Yao, Adeel Pervez, Dan Alistarh, Francesco Locatello |
| 2025 | ICLR | How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations. | Siddhartha Gairola, Moritz Bhle, Francesco Locatello, Bernt Schiele |
| 2025 | ICLR | Near, far: Patch-ordering enhances vision foundation models' scene understanding. | Valentinos Pariza, Mohammadreza Salehi, Gertjan J. Burghouts, Francesco Locatello, Yuki M. Asano |
| 2025 | ICLR | Unifying Causal Representation Learning with the Invariance Principle. | Dingling Yao, Dario Rancati, Riccardo Cadei, Marco Fumero, Francesco Locatello |
| 2025 | ICML | Mechanistic PDE Networks for Discovery of Governing Equations. | Adeel Pervez, Efstratios Gavves, Francesco Locatello |
| 2024 | AISTATS | Self-Compatibility: Evaluating Causal Discovery without Ground Truth. | Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing |
| 2024 | CVPR | Adaptive Slot Attention: Object Discovery with Dynamic Slot Number. | Ke Fan, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco Locatello, Zheng Zhang |
| 2024 | ICLR | Grounded Object-Centric Learning. | Avinash Kori, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni, Ben Glocker |
| 2024 | ICLR | Multi-View Causal Representation Learning with Partial Observability. | Dingling Yao, Danru Xu, Sbastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kgelgen, Francesco Locatello |
| 2024 | ICML | Unsupervised Concept Discovery Mitigates Spurious Correlations. | Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi |
| 2024 | ICML | Mechanistic Neural Networks for Scientific Machine Learning. | Adeel Pervez, Francesco Locatello, Stratis Gavves |
| 2024 | ICML | A Sparsity Principle for Partially Observable Causal Representation Learning. | Danru Xu, Dingling Yao, Sbastien Lachapelle, Perouz Taslakian, Julius von Kgelgen, Francesco Locatello, Sara Magliacane |
| 2023 | ICCV | Unsupervised Open-Vocabulary Object Localization in Videos. | Ke Fan, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao, Carl-Johann Simon-Gabriel, Mike Zheng Shou, Francesco Locatello, Bernt Schiele, Thomas Brox, Zheng Zhang, Yanwei Fu, Tong He |
| 2023 | ICCV | Object-Centric Multiple Object Tracking. | Zixu Zhao, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik Zietlow, Carl-Johann Simon-Gabriel, Bing Shuai, Zhuowen Tu, Thomas Brox, Bernt Schiele, Yanwei Fu, Francesco Locatello, Zheng Zhang, Tianjun Xiao |
| 2023 | ICLR | Relative representations enable zero-shot latent space communication. | Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodol |
| 2023 | ICLR | Bridging the Gap to Real-World Object-Centric Learning. | Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schlkopf, Thomas Brox, Francesco Locatello |
| 2023 | ICLR | Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. | Andrii Zadaianchuk, Matthus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox |
| 2023 | ICML | Benign Overfitting in Deep Neural Networks under Lazy Training. | Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher |
| 2023 | WACV | TeST: Test-time Self-Training under Distribution Shift. | Samarth Sinha, Peter V. Gehler, Francesco Locatello, Bernt Schiele |
| 2022 | AISTATS | Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. | Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rtsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever |
| 2022 | CVPR | Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. | Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthus Kleindessner, Francesco Locatello, Bernhard Schlkopf, Chris Russell |
| 2022 | ICLR | You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. | Osama Makansi, Julius von Kgelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schlkopf |
| 2022 | ICLR | Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. | Lukas Schott, Julius von Kgelgen, Frederik Truble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schlkopf, Francesco Locatello, Wieland Brendel |
| 2022 | ICLR | The Role of Pretrained Representations for the OOD Generalization of RL Agents. | Frederik Truble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schlkopf, Stefan Bauer |
| 2022 | ICML | Generalization and Robustness Implications in Object-Centric Learning. | Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schlkopf, Ole Winther, Francesco Locatello |
| 2022 | ICML | Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. | Paul Rolland, Volkan Cevher, Matthus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schlkopf, Francesco Locatello |
| 2021 | ICLR | On the Transfer of Disentangled Representations in Realistic Settings. | Andrea Dittadi, Frederik Truble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schlkopf |
| 2021 | ICML | On Disentangled Representations Learned from Correlated Data. | Frederik Truble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schlkopf, Stefan Bauer |
| 2021 | ICML | Neighborhood Contrastive Learning Applied to Online Patient Monitoring. | Hugo Yche, Gideon Dresdner, Francesco Locatello, Matthias Hser, Gunnar Rtsch |
| 2021 | IJCAI | Boosting Variational Inference With Locally Adaptive Step-Sizes. | Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rtsch |
| 2020 | AAAI | A Commentary on the Unsupervised Learning of Disentangled Representations. | Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rtsch, Sylvain Gelly, Bernhard Schlkopf, Olivier Bachem |
| 2020 | ICLR | Disentangling Factors of Variations Using Few Labels. | Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rtsch, Bernhard Schlkopf, Olivier Bachem |
| 2020 | ICML | Weakly-Supervised Disentanglement Without Compromises. | Francesco Locatello, Ben Poole, Gunnar Rtsch, Bernhard Schlkopf, Olivier Bachem, Michael Tschannen |
| 2020 | ICML | Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. | Geoffrey Ngiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa |
| 2019 | ICLR | SOM-VAE: Interpretable Discrete Representation Learning on Time Series. | Vincent Fortuin, Matthias Hser, Francesco Locatello, Heiko Strathmann, Gunnar Rtsch |
| 2019 | ICLR | Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. | Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rtsch, Sylvain Gelly, Bernhard Schlkopf, Olivier Bachem |
| 2019 | ICML | Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. | Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rtsch, Sylvain Gelly, Bernhard Schlkopf, Olivier Bachem |
| 2019 | UAI | The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. | Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schlkopf |
| 2018 | AISTATS | Boosting Variational Inference: an Optimization Perspective. | Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rtsch |
| 2018 | ICLR | Clustering Meets Implicit Generative Models. | Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rtsch, Sylvain Gelly, Bernhard Schlkopf |
| 2018 | ICML | On Matching Pursuit and Coordinate Descent. | Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rtsch, Bernhard Schlkopf, Sebastian U. Stich, Martin Jaggi |
| 2018 | ICML | A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming. | Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher |
| 2017 | AISTATS | A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. | Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi |