Locatello, Francesco

99 publications

ICLRW 2025 Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences Riccardo Cadei, Ilker Demirel, Piersilvio De Bartolomeis, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello

ICLRW 2025 Causal Representation Learning and Inference via Mixture-Based Priors Avinash Kori, Carles Balsells-Rodas, Ben Glocker, Yingzhen Li, Francesco Locatello

NeurIPS 2025 Connecting Neural Models Latent Geometries with Relative Geodesic Representations Hanlin Yu, Berfin Inal, Georgios Arvanitidis, Søren Hauberg, Francesco Locatello, Marco Fumero

TMLR 2025 Demystifying Amortized Causal Discovery with Transformers Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello

NeurIPS 2025 Head Pursuit: Probing Attention Specialization in Multimodal Transformers Lorenzo Basile, Valentino Maiorca, Diego Doimo, Francesco Locatello, Alberto Cazzaniga

ICLR 2025 How to Probe: Simple yet Effective Techniques for Improving Post-Hoc Explanations Siddhartha Gairola, Moritz Böhle, Francesco Locatello, Bernt Schiele

ICML 2025 Mechanistic PDE Networks for Discovery of Governing Equations Adeel Pervez, Efstratios Gavves, Francesco Locatello

ICLR 2025 Near, Far: Patch-Ordering Enhances Vision Foundation Models' Scene Understanding Valentinos Pariza, Mohammadreza Salehi, Gertjan J. Burghouts, Francesco Locatello, Yuki M Asano

NeurIPS 2025 Out-of-Distribution Detection with Relative Angles Berker Demirel, Marco Fumero, Francesco Locatello

NeurIPS 2025 Prediction-Powered Causal Inferences Riccardo Cadei, Ilker Demirel, Piersilvio De Bartolomeis, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello

TMLR 2025 ResiDual Transformer Alignment with Spectral Decomposition Lorenzo Basile, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà, Francesco Locatello

ICLR 2025 Scalable Mechanistic Neural Networks Jiale Chen, Dingling Yao, Adeel Pervez, Dan Alistarh, Francesco Locatello

CLeaR 2025 Score Matching Through the Roof: Linear, Nonlinear, and Latent Variables Causal Discovery Francesco Montagna, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Francesco Locatello

NeurIPS 2025 The Third Pillar of Causal Analysis? a Measurement Perspective on Causal Representations Dingling Yao, Shimeng Huang, Riccardo Cadei, Kun Zhang, Francesco Locatello

ICLR 2025 Unifying Causal Representation Learning with the Invariance Principle Dingling Yao, Dario Rancati, Riccardo Cadei, Marco Fumero, Francesco Locatello

ICLRW 2025 Unifying Causal and Object-Centric Representation Learning Allows Causal Composition Avinash Kori, Ben Glocker, Bernhard Schölkopf, Francesco Locatello

ICML 2024 A Sparsity Principle for Partially Observable Causal Representation Learning Danru Xu, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius Von Kügelgen, Francesco Locatello, Sara Magliacane

CVPR 2024 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

NeurIPSW 2024 Boosting Unsupervised Segmentation Learning Alp Eren Sari, Francesco Locatello, Paolo Favaro

ICMLW 2024 Demystifying Amortized Causal Discovery with Transformers Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello

ICLR 2024 Grounded Object-Centric Learning Avinash Kori, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni, Ben Glocker

NeurIPS 2024 Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, Fabio De Sousa Ribeiro

NeurIPS 2024 Identifying General Mechanism Shifts in Linear Causal Representations Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar

ICMLW 2024 Latent Functional Maps Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà

ICMLW 2024 Latent Functional Maps Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà

NeurIPS 2024 Latent Functional Maps: A Spectral Framework for Representation Alignment Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà

NeurIPS 2024 Marrying Causal Representation Learning with Dynamical Systems for Science Dingling Yao, Caroline Muller, Francesco Locatello

ICMLW 2024 Marrying Causal Representation Learning with Dynamical Systems for Science Dingling Yao, Caroline Muller, Francesco Locatello

ICML 2024 Mechanistic Neural Networks for Scientific Machine Learning Adeel Pervez, Francesco Locatello, Stratis Gavves

ICLRW 2024 Mechanistic Neural Networks for Scientific Machine Learning Adeel Pervez, Francesco Locatello, Stratis Gavves

ICLR 2024 Multi-View Causal Representation Learning with Partial Observability Dingling Yao, Danru Xu, Sebastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello

ICMLW 2024 Scalable Unsupervised Alignment of Metric and Nonmetric Structures Sanketh Vedula, Valentino Maiorca, Lorenzo Basile, Francesco Locatello, Alexander Bronstein

AISTATS 2024 Self-Compatibility: Evaluating Causal Discovery Without Ground Truth Philipp M. Faller, Leena C. Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing

NeurIPS 2024 Smoke and Mirrors in Causal Downstream Tasks Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello

ICMLW 2024 Smoke and Mirrors in Causal Downstream Tasks Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello

NeurIPSW 2024 Unifying Causal Representation Learning with the Invariance Principle Dingling Yao, Dario Rancati, Riccardo Cadei, Marco Fumero, Francesco Locatello

NeurIPSW 2024 Unifying Causal Representation Learning with the Invariance Principle Dingling Yao, Dario Rancati, Riccardo Cadei, Marco Fumero, Francesco Locatello

ICML 2024 Unsupervised Concept Discovery Mitigates Spurious Correlations Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi

NeurIPSW 2023 A Sparsity Principle for Partially Observable Causal Representation Learning Danru Xu, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane

NeurIPS 2023 ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, Francesco Locatello

NeurIPS 2023 Assumption Violations in Causal Discovery and the Robustness of Score Matching Francesco Montagna, Atalanti Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello

ICML 2023 Benign Overfitting in Deep Neural Networks Under Lazy Training Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher

ICLR 2023 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 Schölkopf, Thomas Brox, Francesco Locatello

CLeaR 2023 Causal Discovery with Score Matching on Additive Models with Arbitrary Noise Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

CLeaR 2023 Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello

TMLR 2023 Image Retrieval Outperforms Diffusion Models on Data Augmentation Max F Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell

NeurIPS 2023 Latent Space Translation via Semantic Alignment Valentino Maiorca, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco Locatello, Emanuele Rodolà

NeurIPS 2023 Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello

NeurIPSW 2023 Multi-View Causal Representation Learning with Partial Observability Dingling Yao, Danru Xu, Sebastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello

ICCV 2023 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

ICLR 2023 Relative Representations Enable Zero-Shot Latent Space Communication Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà

NeurIPS 2023 Rotating Features for Object Discovery Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling

NeurIPS 2023 Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling Zhenyu Zhu, Francesco Locatello, Volkan Cevher

CLeaR 2023 Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

WACV 2023 TeST: Test-Time Self-Training Under Distribution Shift Samarth Sinha, Peter Gehler, Francesco Locatello, Bernt Schiele

CLeaR 2023 Unsupervised Object Learning via Common Fate Matthias Tangemann, Steffen Schneider, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kuemmerer, Matthias Bethge, Bernhard Schölkopf

ICCV 2023 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

ICLR 2023 Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations Andrii Zadaianchuk, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox

AISTATS 2022 Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever

NeurIPS 2022 Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell

NeurIPS 2022 Assaying Out-of-Distribution Generalization in Transfer Learning Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello

ICLRW 2022 Compositional Multi-Object Reinforcement Learning with Linear Relation Networks Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello

ICML 2022 Generalization and Robustness Implications in Object-Centric Learning Andrea Dittadi, Samuele S Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello

CVPR 2022 Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell

NeurIPS 2022 Neural Attentive Circuits Martin Weiss, Nasim Rahaman, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Erran Li Li, Nicolas Ballas

NeurIPSW 2022 Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

ICML 2022 Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello

NeurIPS 2022 Self-Supervised Amodal Video Object Segmentation Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P Wipf, Yanwei Fu, Zheng Zhang

ICLR 2022 The Role of Pretrained Representations for the OOD Generalization of RL Agents Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

ICLR 2022 Visual Representation Learning Does Not Generalize Strongly Within the Same Domain Lukas Schott, Julius Von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

ICLR 2022 You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction Osama Makansi, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

NeurIPS 2021 Backward-Compatible Prediction Updates: A Probabilistic Approach Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler

IJCAI 2021 Boosting Variational Inference with Locally Adaptive Step-Sizes Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch

NeurIPS 2021 Dynamic Inference with Neural Interpreters Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf

ICML 2021 Neighborhood Contrastive Learning Applied to Online Patient Monitoring Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch

ICML 2021 On Disentangled Representations Learned from Correlated Data Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer

ICLR 2021 On the Transfer of Disentangled Representations in Realistic Settings Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf

ICMLW 2021 Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

NeurIPS 2021 Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello

AAAI 2020 A Commentary on the Unsupervised Learning of Disentangled Representations Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

JMLR 2020 A Sober Look at the Unsupervised Learning of Disentangled Representations and Their Evaluation Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

ICLR 2020 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2020 Object-Centric Learning with Slot Attention Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf

ICML 2020 Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa

ICML 2020 Weakly-Supervised Disentanglement Without Compromises Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen

NeurIPS 2019 Are Disentangled Representations Helpful for Abstract Visual Reasoning? Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

ICML 2019 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

ICLRW 2019 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar R¨¨ätsch, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2019 On the Fairness of Disentangled Representations Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2019 On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

ICLR 2019 SOM-VAE: Interpretable Discrete Representation Learning on Time Series Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

NeurIPS 2019 Stochastic Frank-Wolfe for Composite Convex Minimization Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher

UAI 2019 The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf

ICML 2018 A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher

NeurIPS 2018 Boosting Black Box Variational Inference Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch

AISTATS 2018 Boosting Variational Inference: An Optimization Perspective Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch

ICML 2018 On Matching Pursuit and Coordinate Descent Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi

AISTATS 2017 A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi

NeurIPS 2017 Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi