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ORCIDFrancesco Locatello
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- affiliation: Institute of Science and Technology Austria, Klosterneuburg, Austria
- affiliation (former): Amazon, Tübingen, Germany
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2020 – today
- 2024
[c68]Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. AISTATS 2024: 4132-4140- [c67]Ke Fan, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco Locatello, Zheng Zhang:
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number. CVPR 2024: 23062-23071 - [c66]Avinash Kori, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni, Ben Glocker:
Grounded Object-Centric Learning. ICLR 2024 - [c65]Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello:
Multi-View Causal Representation Learning with Partial Observability. ICLR 2024 - [c64]Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi:
Unsupervised Concept Discovery Mitigates Spurious Correlations. ICML 2024 - [c63]Adeel Pervez, Francesco Locatello, Stratis Gavves:
Mechanistic Neural Networks for Scientific Machine Learning. ICML 2024 - [c62]Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane:
A Sparsity Principle for Partially Observable Causal Representation Learning. ICML 2024 - [c61]Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello:
Smoke and Mirrors in Causal Downstream Tasks. NeurIPS 2024 - [c60]Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar:
Identifying General Mechanism Shifts in Linear Causal Representations. NeurIPS 2024 - [c59]Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà:
Latent Functional Maps: a spectral framework for representation alignment. NeurIPS 2024 - [c58]Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, Fabio De Sousa Ribeiro:
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention. NeurIPS 2024 - [c57]Dingling Yao, Caroline Muller, Francesco Locatello:
Marrying Causal Representation Learning with Dynamical Systems for Science. NeurIPS 2024 - [e2]Francesco Locatello, Vanessa Didelez:
Causal Learning and Reasoning, 1-3 April 2024, Los Angeles, California, USA. Proceedings of Machine Learning Research 236, PMLR 2024 [contents] - [i85]Sindy Löwe, Francesco Locatello, Max Welling:
Binding Dynamics in Rotating Features. CoRR abs/2402.05627 (2024) - [i84]Adeel Pervez, Francesco Locatello, Efstratios Gavves:
Mechanistic Neural Networks for Scientific Machine Learning. CoRR abs/2402.13077 (2024) - [i83]Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi:
Unsupervised Concept Discovery Mitigates Spurious Correlations. CoRR abs/2402.13368 (2024) - [i82]Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane:
A Sparsity Principle for Partially Observable Causal Representation Learning. CoRR abs/2403.08335 (2024) - [i81]Alp Eren Sari, Francesco Locatello, Paolo Favaro:
Two Tricks to Improve Unsupervised Segmentation Learning. CoRR abs/2404.03392 (2024) - [i80]Dingling Yao, Caroline Muller, Francesco Locatello:
Marrying Causal Representation Learning with Dynamical Systems for Science. CoRR abs/2405.13888 (2024) - [i79]Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello:
Demystifying amortized causal discovery with transformers. CoRR abs/2405.16924 (2024) - [i78]Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello:
Smoke and Mirrors in Causal Downstream Tasks. CoRR abs/2405.17151 (2024) - [i77]Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, Fabio De Sousa Ribeiro:
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention. CoRR abs/2406.07141 (2024) - [i76]Ke Fan, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco Locatello, Zheng Zhang:
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number. CoRR abs/2406.09196 (2024) - [i75]Sanketh Vedula, Valentino Maiorca, Lorenzo Basile, Francesco Locatello, Alexander M. Bronstein:
Scalable unsupervised alignment of general metric and non-metric structures. CoRR abs/2406.13507 (2024) - [i74]Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà:
Latent Functional Maps. CoRR abs/2406.14183 (2024) - [i73]Valentino Maiorca, Luca Moschella, Marco Fumero, Francesco Locatello, Emanuele Rodolà:
Latent Space Translation via Inverse Relative Projection. CoRR abs/2406.15057 (2024) - [i72]Francesco Montagna, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Francesco Locatello:
Score matching through the roof: linear, nonlinear, and latent variables causal discovery. CoRR abs/2407.18755 (2024) - [i71]Valentinos Pariza, Mohammadreza Salehi, Gertjan J. Burghouts, Francesco Locatello, Yuki M. Asano:
NeCo: Improving DINOv2's spatial representations in 19 GPU hours with Patch Neighbor Consistency. CoRR abs/2408.11054 (2024) - [i70]Dingling Yao, Dario Rancati, Riccardo Cadei, Marco Fumero, Francesco Locatello:
Unifying Causal Representation Learning with the Invariance Principle. CoRR abs/2409.02772 (2024) - [i69]Berker Demirel, Lingjing Kong, Kun Zhang, Theofanis Karaletsos, Celestine Mendler-Dünner, Francesco Locatello:
Adjusting Pretrained Backbones for Performativity. CoRR abs/2410.04499 (2024) - [i68]Berker Demirel, Marco Fumero, Francesco Locatello:
Look Around and Find Out: OOD Detection with Relative Angles. CoRR abs/2410.04525 (2024) - [i67]Jiale Chen, Dingling Yao, Adeel Pervez, Dan Alistarh, Francesco Locatello:
Scalable Mechanistic Neural Networks. CoRR abs/2410.06074 (2024) - [i66]Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar:
Identifying General Mechanism Shifts in Linear Causal Representations. CoRR abs/2410.24059 (2024) - [i65]Lorenzo Basile, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà, Francesco Locatello:
ResiDual Transformer Alignment with Spectral Decomposition. CoRR abs/2411.00246 (2024) - 2023
- [j4]Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell:
Image retrieval outperforms diffusion models on data augmentation. Trans. Mach. Learn. Res. 2023 (2023) - [c56]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CLeaR 2023: 281-327 - [c55]Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CLeaR 2023: 553-573 - [c54]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CLeaR 2023: 726-751 - [c53]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CLeaR 2023: 752-771 - [c52]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:
Unsupervised Open-Vocabulary Object Localization in Videos. ICCV 2023: 13701-13709 - [c51]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:
Object-Centric Multiple Object Tracking. ICCV 2023: 16555-16565 - [c50]Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà:
Relative representations enable zero-shot latent space communication. ICLR 2023 - [c49]Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello:
Bridging the Gap to Real-World Object-Centric Learning. ICLR 2023 - [c48]Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox:
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. ICLR 2023 - [c47]Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher:
Benign Overfitting in Deep Neural Networks under Lazy Training. ICML 2023: 43105-43128 - [c46]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. NeurIPS 2023 - [c45]Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling:
Rotating Features for Object Discovery. NeurIPS 2023 - [c44]Valentino Maiorca, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco Locatello, Emanuele Rodolà:
Latent Space Translation via Semantic Alignment. NeurIPS 2023 - [c43]Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello:
Assumption violations in causal discovery and the robustness of score matching. NeurIPS 2023 - [c42]Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, Francesco Locatello:
ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training. NeurIPS 2023 - [c41]Zhenyu Zhu, Francesco Locatello, Volkan Cevher:
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling. NeurIPS 2023 - [c40]Marco Fumero, Emanuele Rodolà, Clémentine Dominé, Francesco Locatello, Karolina Dziugaite, Mathilde Caron:
Preface of UniReps: the First Workshop on Unifying Representations in Neural Models. UniReps 2023: 1-10 - [c39]Samarth Sinha, Peter V. Gehler, Francesco Locatello, Bernt Schiele
:
TeST: Test-time Self-Training under Distribution Shift. WACV 2023: 2758-2768 - [e1]Marco Fumero, Emanuele Rodolà, Clémentine Dominé, Francesco Locatello, Karolina Dziugaite, Mathilde Caron:
Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 15 December 2023, Ernest N. Morial Convention Center, New Orleans, USA. Proceedings of Machine Learning Research 243, PMLR 2023 [contents] - [i64]Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CoRR abs/2301.05169 (2023) - [i63]Dong Lao, Zhengyang Hu, Francesco Locatello, Yanchao Yang, Stefano Soatto:
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots. CoRR abs/2304.01430 (2023) - [i62]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CoRR abs/2304.03265 (2023) - [i61]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CoRR abs/2304.03382 (2023) - [i60]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. CoRR abs/2304.07939 (2023) - [i59]Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell:
A data augmentation perspective on diffusion models and retrieval. CoRR abs/2304.10253 (2023) - [i58]Zhenyu Zhu, Fanghui Liu, Grigorios G. Chrysos, Francesco Locatello, Volkan Cevher:
Benign Overfitting in Deep Neural Networks under Lazy Training. CoRR abs/2305.19377 (2023) - [i57]Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling:
Rotating Features for Object Discovery. CoRR abs/2306.00600 (2023) - [i56]Avinash Kori, Francesco Locatello, Francesca Toni, Ben Glocker:
Unsupervised Conditional Slot Attention for Object Centric Learning. CoRR abs/2307.09437 (2023) - [i55]Philipp Michael Faller, Leena Chennuru Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. CoRR abs/2307.09552 (2023) - [i54]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:
Object-Centric Multiple Object Tracking. CoRR abs/2309.00233 (2023) - [i53]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:
Unsupervised Open-Vocabulary Object Localization in Videos. CoRR abs/2309.09858 (2023) - [i52]Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello:
Assumption violations in causal discovery and the robustness of score matching. CoRR abs/2310.13387 (2023) - [i51]Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Francesco Locatello:
Shortcuts for causal discovery of nonlinear models by score matching. CoRR abs/2310.14246 (2023) - [i50]Zhenyu Zhu, Francesco Locatello, Volkan Cevher:
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling. CoRR abs/2310.18123 (2023) - [i49]Valentino Maiorca, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco Locatello, Emanuele Rodolà:
Latent Space Translation via Semantic Alignment. CoRR abs/2311.00664 (2023) - [i48]Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello:
Multi-View Causal Representation Learning with Partial Observability. CoRR abs/2311.04056 (2023) - 2022
- [c38]Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever:
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. AISTATS 2022: 8439-8457 - [c37]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell:
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CVPR 2022: 10400-10411 - [c36]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR 2022 - [c35]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR 2022 - [c34]Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
The Role of Pretrained Representations for the OOD Generalization of RL Agents. ICLR 2022 - [c33]Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello:
Generalization and Robustness Implications in Object-Centric Learning. ICML 2022: 5221-5285 - [c32]Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello:
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML 2022: 18741-18753 - [c31]Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. NeurIPS 2022 - [c30]Martin Weiss, Nasim Rahaman, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas:
Neural Attentive Circuits. NeurIPS 2022 - [c29]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:
Assaying Out-Of-Distribution Generalization in Transfer Learning. NeurIPS 2022 - [c28]Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P. Wipf, Yanwei Fu, Zheng Zhang:
Self-supervised Amodal Video Object Segmentation. NeurIPS 2022 - [i47]Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello:
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks. CoRR abs/2201.13388 (2022) - [i46]Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever:
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. CoRR abs/2202.13212 (2022) - [i45]Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello:
Score matching enables causal discovery of nonlinear additive noise models. CoRR abs/2203.04413 (2022) - [i44]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell:
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CoRR abs/2203.04913 (2022) - [i43]Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell:
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. CoRR abs/2204.04440 (2022) - [i42]Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox:
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. CoRR abs/2207.05027 (2022) - [i41]Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. CoRR abs/2207.09239 (2022) - [i40]Samarth Sinha, Peter V. Gehler, Francesco Locatello, Bernt Schiele:
TeST: Test-time Self-Training under Distribution Shift. CoRR abs/2209.11459 (2022) - [i39]Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello:
Bridging the Gap to Real-World Object-Centric Learning. CoRR abs/2209.14860 (2022) - [i38]Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà:
Relative representations enable zero-shot latent space communication. CoRR abs/2209.15430 (2022) - [i37]Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, Francesco Locatello:
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training. CoRR abs/2210.01738 (2022) - [i36]Nasim Rahaman, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas:
Neural Attentive Circuits. CoRR abs/2210.08031 (2022) - [i35]Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, Zheng Zhang:
Self-supervised Amodal Video Object Segmentation. CoRR abs/2210.12733 (2022) - [i34]Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf:
A General Purpose Neural Architecture for Geospatial Systems. CoRR abs/2211.02348 (2022) - 2021
- [j3]Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio:
Toward Causal Representation Learning. Proc. IEEE 109(5): 612-634 (2021) - [c27]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. ICLR 2021 - [c26]Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer:
On Disentangled Representations Learned from Correlated Data. ICML 2021: 10401-10412 - [c25]Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch:
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. ICML 2021: 11964-11974 - [c24]Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch:
Boosting Variational Inference With Locally Adaptive Step-Sizes. IJCAI 2021: 2337-2343 - [c23]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS 2021: 116-128 - [c22]Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf:
Dynamic Inference with Neural Interpreters. NeurIPS 2021: 10985-10998 - [c21]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS 2021: 16451-16467 - [i33]Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio:
Towards Causal Representation Learning. CoRR abs/2102.11107 (2021) - [i32]Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch:
Boosting Variational Inference With Locally Adaptive Step-Sizes. CoRR abs/2105.09240 (2021) - [i31]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR abs/2106.04619 (2021) - [i30]Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch:
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. CoRR abs/2106.05142 (2021) - [i29]Andrea Dittadi, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello:
Generalization and Robustness Implications in Object-Centric Learning. CoRR abs/2107.00637 (2021) - [i28]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR abs/2107.01057 (2021) - [i27]Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. CoRR abs/2107.05686 (2021) - [i26]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR abs/2107.08221 (2021) - [i25]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR abs/2110.05304 (2021) - [i24]Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf:
Dynamic Inference with Neural Interpreters. CoRR abs/2110.06399 (2021) - [i23]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CoRR abs/2110.06562 (2021) - [i22]Francesco Locatello:
Enforcing and Discovering Structure in Machine Learning. CoRR abs/2111.13693 (2021) - 2020
- [b1]Francesco Locatello:
Enforcing and Discovering Structure in Machine Learning. ETH Zurich, Zürich, Switzerland, 2020 - [j2]Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Tumor Profiler Consortium, Gunnar Rätsch, Kjong-Van Lehmann:
SCIM: universal single-cell matching with unpaired feature sets. Bioinform. 36(Supplement-2): i919-i927 (2020) - [j1]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21: 209:1-209:62 (2020) - [c20]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI 2020: 13681-13684 - [c19]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. ICLR 2020 - [c18]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. ICML 2020: 6348-6359 - [c17]Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa:
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. ICML 2020: 7253-7262 - [c16]Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf:
Object-Centric Learning with Slot Attention. NeurIPS 2020 - [i21]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. CoRR abs/2002.02886 (2020) - [i20]Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Fabian Pedregosa:
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. CoRR abs/2002.11860 (2020) - [i19]Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer:
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. CoRR abs/2006.07886 (2020) - [i18]Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf:
Object-Centric Learning with Slot Attention. CoRR abs/2006.15055 (2020) - [i17]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR abs/2007.14184 (2020) - [i16]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. CoRR abs/2010.14407 (2020) - [i15]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR abs/2010.14766 (2020)
2010 – 2019
- 2019
- [c15]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) 2019 - [c14]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR 2019 - [c13]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019: 4114-4124 - [c12]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS 2019: 14222-14235 - [c11]Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher:
Stochastic Frank-Wolfe for Composite Convex Minimization. NeurIPS 2019: 14246-14256 - [c10]Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. NeurIPS 2019: 14584-14597 - [c9]Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. NeurIPS 2019: 15714-15725 - [c8]Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. UAI 2019: 217-227 - [i14]Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher:
Stochastic Conditional Gradient Method for Composite Convex Minimization. CoRR abs/1901.10348 (2019) - [i13]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variation Using Few Labels. CoRR abs/1905.01258 (2019) - [i12]Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA. CoRR abs/1905.06642 (2019) - [i11]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR abs/1905.12506 (2019) - [i10]Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. CoRR abs/1905.13662 (2019) - [i9]Muhammad Waleed Gondal, Manuel Wüthrich, Ðorðe Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. CoRR abs/1906.03292 (2019) - 2018
- [c7]Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. AISTATS 2018: 464-472 - [c6]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. ICLR (Workshop) 2018 - [c5]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
On Matching Pursuit and Coordinate Descent. ICML 2018: 3204-3213 - [c4]Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher:
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming. ICML 2018: 5713-5722 - [c3]Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch:
Boosting Black Box Variational Inference. NeurIPS 2018: 3405-3415 - [i8]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR abs/1803.09539 (2018) - [i7]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. CoRR abs/1804.11130 (2018) - [i6]Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch:
Boosting Black Box Variational Inference. CoRR abs/1806.02185 (2018) - [i5]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR abs/1806.02199 (2018) - [i4]Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR abs/1811.12359 (2018) - 2017
- [c2]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS 2017: 860-868 - [c1]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS 2017: 773-784 - [i3]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR abs/1702.06457 (2017) - [i2]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR abs/1705.11041 (2017) - [i1]Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. CoRR abs/1708.01733 (2017)
Coauthor Index
Peter V. Gehler
aka: Peter Vincent Gehler
aka: Peter Vincent Gehler
[c56] [c55] [c49] [c46] [i64] [i60] [c37] [c36] [c35] [c34] [c33] [c32] [c30] [c29] [i47] [i45] [i44] [i41] [i39] [i36] [i34] [j3] [c27] [c26] [c23] [c22] [c21] [i33] [i31] [i29] [i28] [i27] [i26] [i25] [i24] [i23] [j1] [c20] [c19] [c18] [i21] [i19] [i17] [i16] [i15] [c14] [c13] [c10] [c9] [c8] [i13] [i12] [i10] [i9] [c6] [c5] [i8] [i7] [i4]
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