Lao, Dong, et al. “Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots.” 1st Conference on Parsimony and Learning, 2024.
LibreCat: https://research-explorer.ista.ac.at/record/14213
arXiv: 2304.01430

Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.”10.1109/wacv56688.2023.00278__2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.
LibreCat: https://research-explorer.ista.ac.at/record/14105
DOI: 10.1109/wacv56688.2023.00278
arXiv: 2209.11459

Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 43105–28.
LibreCat: https://research-explorer.ista.ac.at/record/14208
arXiv: 2305.19377

Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.”10.48550/arXiv.2304.10253__ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253.
LibreCat: https://research-explorer.ista.ac.at/record/14209
DOI: 10.48550/arXiv.2304.10253
arXiv: 2304.10253

Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14211
arXiv: 2304.03265

Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14212
arXiv: 2304.03382

Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14214
arXiv: 2301.05169

Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space Communication.” The 11th International Conference on Learning Representations, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14217
arXiv: 2209.15430

Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.” 2nd Conference on Causal Learning and Reasoning, 2110.06562, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14222
arXiv: 2110.06562

Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric Learning.” The 11th International Conference on Learning Representations, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14218
arXiv: 2209.14860

Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” The 11th International Conference on Learning Representations, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14219
arXiv: 2207.05027

Faller, Philipp M., et al. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.”10.48550/arXiv.2307.09552__ArXiv, 2307.09552, doi:10.48550/arXiv.2307.09552.
LibreCat: https://research-explorer.ista.ac.at/record/14333
DOI: 10.48550/arXiv.2307.09552
arXiv: 2307.09552

Burg, Max, et al. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” Journal of Machine Learning Research, ML Research Press, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14949

Yao, Dingling, et al. “Multi-View Causal Representation Learning with Partial Observability.”10.48550/arXiv.2311.04056__ArXiv, 2311.04056, doi:10.48550/arXiv.2311.04056.
LibreCat: https://research-explorer.ista.ac.at/record/14946
DOI: 10.48550/arXiv.2311.04056
arXiv: 2311.04056

Maiorca, Valentino, et al. “Latent Space Translation via Semantic Alignment.”10.48550/arXiv.2311.00664__ArXiv, 2311.00664, doi:10.48550/arXiv.2311.00664.
LibreCat: https://research-explorer.ista.ac.at/record/14952
DOI: 10.48550/arXiv.2311.00664
arXiv: 2311.00664

Kori, Avinash, et al. “Grounded Object Centric Learning.”10.48550/arXiv.2307.09437__ArXiv, 2307.09437, doi:10.48550/arXiv.2307.09437.
LibreCat: https://research-explorer.ista.ac.at/record/14948
DOI: 10.48550/arXiv.2307.09437
arXiv: 2307.09437

Zhu, Zhenyu, et al. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.”10.48550/arXiv.2310.18123__ArXiv, 2310.18123, doi:10.48550/arXiv.2310.18123.
LibreCat: https://research-explorer.ista.ac.at/record/14953
DOI: 10.48550/arXiv.2310.18123
arXiv: 2310.18123

Montagna, Francesco, et al. “Assumption Violations in Causal Discovery and the Robustness of Score Matching.”10.48550/arXiv.2310.13387__ArXiv, 2310.13387, doi:10.48550/arXiv.2310.13387.
LibreCat: https://research-explorer.ista.ac.at/record/14954
DOI: 10.48550/arXiv.2310.13387
arXiv: 2310.13387

Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.”10.48550/arXiv.2304.07939__ArXiv, 2304.07939, doi:10.48550/arXiv.2304.07939.
LibreCat: https://research-explorer.ista.ac.at/record/14210
DOI: 10.48550/arXiv.2304.07939
arXiv: 2304.07939

Löwe, Sindy, et al. “Rotating Features for Object Discovery.”10.48550/arXiv.2306.00600__ArXiv, 2306.00600, doi:10.48550/arXiv.2306.00600.
LibreCat: https://research-explorer.ista.ac.at/record/14207
DOI: 10.48550/arXiv.2306.00600
arXiv: 2306.00600

Zhao, Zixu, et al. “Object-Centric Multiple Object Tracking.”10.48550/arXiv.2309.00233__ArXiv, 2309.00233, doi:10.48550/arXiv.2309.00233.
LibreCat: https://research-explorer.ista.ac.at/record/14963
DOI: 10.48550/arXiv.2309.00233
arXiv: 2309.00233

Montagna, Francesco, et al. “Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.”10.48550/arXiv.2310.14246__ArXiv, 2310.14246, doi:10.48550/arXiv.2310.14246.
LibreCat: https://research-explorer.ista.ac.at/record/14961
DOI: 10.48550/arXiv.2310.14246
arXiv: 2310.14246

Fan, Ke, et al. “Unsupervised Open-Vocabulary Object Localization in Videos.”10.48550/arXiv.2309.09858__ArXiv, 2309.09858, doi:10.48550/arXiv.2309.09858.
LibreCat: https://research-explorer.ista.ac.at/record/14962
DOI: 10.48550/arXiv.2309.09858
arXiv: 2309.09858

Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation Learning.” Causal Representation Learning Workshop at NeurIPS 2023, 54, OpenReview, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14958

Zhang, Cheng, et al. “Causality in the Time of LLMs: Round Table Discussion Results of CLeaR 2023.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14974

Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98.
LibreCat: https://research-explorer.ista.ac.at/record/14173
arXiv: 2207.09239

Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 16548–62.
LibreCat: https://research-explorer.ista.ac.at/record/14106
arXiv: 2204.04440

Dresdner, Gideon, et al. “Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, vol. 151, ML Research Press, 2022, pp. 8439–57.
LibreCat: https://research-explorer.ista.ac.at/record/14093
arXiv: 2202.13212

Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.”10.1109/cvpr52688.2022.01016__2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.
LibreCat: https://research-explorer.ista.ac.at/record/14114
DOI: 10.1109/cvpr52688.2022.01016
arXiv: 2203.04913

Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural Information Processing Systems, vol. 35, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/14168
arXiv: 2210.08031

Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric Learning.” Proceedings of the 39th International Conference on Machine Learning, vol. 2022, ML Research Press, pp. 5221–85.
LibreCat: https://research-explorer.ista.ac.at/record/14170
arXiv: 2107.00637

Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” 10th International Conference on Learning Representations, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/14172
arXiv: 2107.08221

Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.”10.48550/arXiv.2210.12733__36th Conference on Neural Information Processing Systems, 2022, doi:10.48550/arXiv.2210.12733.
LibreCat: https://research-explorer.ista.ac.at/record/14107
DOI: 10.48550/arXiv.2210.12733
arXiv: 2210.12733

Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53.
LibreCat: https://research-explorer.ista.ac.at/record/14171
arXiv: 2203.04413

Dittadi, Andrea, et al. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” 10th International Conference on Learning Representations, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/14174
arXiv: 2107.05686

Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” 10th International Conference on Learning Representations, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/14175
arXiv: 2110.05304

Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.”10.48550/arXiv.2201.13388__ArXiv, 2201.13388, doi:10.48550/arXiv.2201.13388.
LibreCat: https://research-explorer.ista.ac.at/record/14220
DOI: 10.48550/arXiv.2201.13388
arXiv: 2201.13388

Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” 36th Conference on Neural Information Processing Systems.
LibreCat: https://research-explorer.ista.ac.at/record/14215
arXiv: 2211.02348

Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.”10.48550/arXiv.2210.01738__ArXiv, 2210.01738, doi:10.48550/arXiv.2210.01738.
LibreCat: https://research-explorer.ista.ac.at/record/14216
DOI: 10.48550/arXiv.2210.01738
arXiv: 2210.01738

Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 10401–12.
LibreCat: https://research-explorer.ista.ac.at/record/14177
arXiv: 2006.07886

Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” Proceedings of 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 11964–74.
LibreCat: https://research-explorer.ista.ac.at/record/14176
arXiv: 2106.05142

Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” 35th Conference on Neural Information Processing Systems, vol. 34, 2021, pp. 116–28.
LibreCat: https://research-explorer.ista.ac.at/record/14182
arXiv: 2107.01057

Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”10.24963/ijcai.2021/322__Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–43, doi:10.24963/ijcai.2021/322.
LibreCat: https://research-explorer.ista.ac.at/record/14181
DOI: 10.24963/ijcai.2021/322
arXiv: 2105.09240

Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 16451–67.
LibreCat: https://research-explorer.ista.ac.at/record/14179
arXiv: 2106.04619

Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98.
LibreCat: https://research-explorer.ista.ac.at/record/14180
arXiv: 2110.06399

Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.”10.1109/jproc.2021.3058954__Proceedings of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:10.1109/jproc.2021.3058954.
LibreCat: https://research-explorer.ista.ac.at/record/14117
DOI: 10.1109/jproc.2021.3058954
arXiv: 2102.11107

Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” The Ninth International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/14178
arXiv: 2010.14407

Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”10.48550/arXiv.2111.13693__ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693.
LibreCat: https://research-explorer.ista.ac.at/record/14221
DOI: 10.48550/arXiv.2111.13693
arXiv: 2111.13693

Träuble, Frederik, et al. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/14332

Stark, Stefan G., et al. “SCIM: Universal Single-Cell Matching with Unpaired Feature Sets.”10.1093/bioinformatics/btaa843__Bioinformatics, vol. 36, no. Supplement_2, Oxford University Press, 2020, pp. i919–27, doi:10.1093/bioinformatics/btaa843.
LibreCat: https://research-explorer.ista.ac.at/record/14125
DOI: 10.1093/bioinformatics/btaa843
PMID: 33381818

Locatello, Francesco, et al. “A Commentary on the Unsupervised Learning of Disentangled Representations.”10.1609/aaai.v34i09.7120__The 34th AAAI Conference on Artificial Intelligence, vol. 34, no. 9, Association for the Advancement of Artificial Intelligence, 2020, pp. 13681–84, doi:10.1609/aaai.v34i09.7120.
LibreCat: https://research-explorer.ista.ac.at/record/14186
DOI: 10.1609/aaai.v34i09.7120
arXiv: 2007.14184

Locatello, Francesco, et al. “Weakly-Supervised Disentanglement without Compromises.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, 2020, pp. 6348–6359.
LibreCat: https://research-explorer.ista.ac.at/record/14188
arXiv: 2002.02886

Négiar, Geoffrey, et al. “Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, 2020, pp. 7253–62.
LibreCat: https://research-explorer.ista.ac.at/record/14187
arXiv: 2002.11860

Locatello, Francesco, et al. “A Sober Look at the Unsupervised Learning of Disentangled Representations and Their Evaluation.” Journal of Machine Learning Research, vol. 21, 209, MIT Press, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/14195
arXiv: 2010.14766

Locatello, Francesco, et al. “Object-Centric Learning with Slot Attention.” Advances in Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 11525–38.
LibreCat: https://research-explorer.ista.ac.at/record/14326
arXiv: 2006.15055

Locatello, Francesco, et al. “Disentangling Factors of Variation Using Few Labels.” 8th International Conference on Learning Representations, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/14184
arXiv: 1905.01258

Gresele, Luigi, et al. “The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA.” Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence, vol. 115, ML Research Press, 2019, pp. 217–27.
LibreCat: https://research-explorer.ista.ac.at/record/14189
arXiv: 1905.06642

Locatello, Francesco, et al. “On the Fairness of Disentangled Representations.” Advances in Neural Information Processing Systems, vol. 32, 2019, pp. 14611–14624.
LibreCat: https://research-explorer.ista.ac.at/record/14197
arXiv: 1905.13662

Locatello, Francesco, et al. “Stochastic Frank-Wolfe for Composite Convex Minimization.” Advances in Neural Information Processing Systems, vol. 32, 2019, pp. 14291–14301.
LibreCat: https://research-explorer.ista.ac.at/record/14191
arXiv: 1901.10348

Steenkiste, Sjoerd van, et al. “Are Disentangled Representations Helpful for Abstract Visual Reasoning?” Advances in Neural Information Processing Systems, vol. 32, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/14193
arXiv: 1905.12506

Locatello, Francesco, et al. “Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 4114–24.
LibreCat: https://research-explorer.ista.ac.at/record/14200
arXiv: 1811.12359

Gondal, Muhammad Waleed, et al. “On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset.” Advances in Neural Information Processing Systems, vol. 32, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/14190
arXiv: 1906.03292

Locatello, Francesco, et al. “Boosting Black Box Variational Inference.” Advances in Neural Information Processing Systems, vol. 31, Neural Information Processing Systems Foundation, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/14202
arXiv: 1806.02185

Locatello, Francesco, et al. “Boosting Variational Inference: An Optimization Perspective.” Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, vol. 84, ML Research Press, 2018, pp. 464–72.
LibreCat: https://research-explorer.ista.ac.at/record/14201
arXiv: 1708.01733

Fortuin, Vincent, et al. “SOM-VAE: Interpretable Discrete Representation Learning on Time Series.” International Conference on Learning Representations, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/14198
arXiv: 1806.02199

Yurtsever, Alp, et al. “A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 5727–36.
LibreCat: https://research-explorer.ista.ac.at/record/14203
arXiv: 1804.08544

Locatello, Francesco, et al. “On Matching Pursuit and Coordinate Descent.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 3198–207.
LibreCat: https://research-explorer.ista.ac.at/record/14204
arXiv: 1803.09539

Locatello, Francesco, et al. “Clustering Meets Implicit Generative Models.” 6th International Conference on Learning Representations, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/14224
arXiv: 1804.11130

Locatello, Francesco, et al. “Competitive Training of Mixtures of Independent Deep Generative Models.”10.48550/arXiv.1804.11130__ArXiv, 1804.11130, doi:10.48550/arXiv.1804.11130.
LibreCat: https://research-explorer.ista.ac.at/record/14327
DOI: 10.48550/arXiv.1804.11130
arXiv: 1804.11130

Locatello, Francesco, et al. “Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees.” Advances in Neural Information Processing Systems, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/14206
arXiv: 1705.11041

Locatello, Francesco, et al. “A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe.” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54, ML Research Press, 2017, pp. 860–68.
LibreCat: https://research-explorer.ista.ac.at/record/14205
arXiv: 1702.06457