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Robust Machine Learning Conference Paper Cross-Entropy Is All You Need To Invert the Data Generating Process Reizinger, P., Bizeul, A., Juhos, A., Vogt, J. E., Balestriero, R., Brendel, W., Klindt, D. In January 2025 (Published) OpenReview BibTeX
Robust Machine Learning Conference Paper Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning Reizinger, P., Guo, S., Huszár, F., Schölkopf, B., Brendel, W. In January 2025 (Published) OpenReview BibTeX
Robust Machine Learning Conference Paper In Search of Forgotten Domain Generalization Mayilvahanan, P., Zimmermann, R. S., Wiedemer, T., Rusak, E., Juhos, A., Bethge, M., Brendel, W. In January 2025 (Published) OpenReview BibTeX
Robust Machine Learning Conference Paper Interaction Asymmetry: A General Principle for Learning Composable Abstractions Brady, J., von Kügelgen, J., Lachapelle, S., Buchholz, S., Kipf, T., Brendel, W. In January 2025 (Published) OpenReview BibTeX
Robust Machine Learning Article Interaction Asymmetry: A General Principle for Learning Composable Abstractions Brady, J., von Kügelgen, J., Lachapelle, S., Buchholz, S., Kipf, T., Brendel, W. November 2024, Submitted (Submitted) BibTeX
Robust Machine Learning Conference Paper Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts Mészáros, A., Ujváry, S., Brendel, W., Reizinger, P., Huszár, F. In September 2024 (Published) ArXiv BibTeX
Empirical Inference Robust Machine Learning Conference Paper Position: Understanding LLMs Requires More Than Statistical Generalization Reizinger, P., Ujváry, S., Mészáros, A., Kerekes, A., Brendel, W., Huszár, F. Proceedings of the 41st International Conference on Machine Learning (ICML), 235:42365-42390, Proceedings of Machine Learning Research, (Editors: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix), PMLR, July 2024 (Published) arXiv URL BibTeX
Robust Machine Learning Conference Paper An interventional perspective on identifiability in gaussian lti systems with independent component analysis Rajendran, G., Reizinger, P., Brendel, W., Ravikumar, P. K. :41-70, Causal Learning and Reasoning, March 2024 (Published) PMLR BibTeX
Robust Machine Learning Conference Paper Does CLIP’s Generalization Performance Mainly Stem from High Train-Test Similarity? Mayilvahanan, P., Wiedemer, T., Rusak, E., Bethge, M., Brendel, W. In June 2024 (Published) ArXiv BibTeX
Robust Machine Learning Conference Paper Don’t trust your eyes: on the (un) reliability of feature visualizations Geirhos, R., Zimmermann, R. S., Bilodeau, B., Brendel, W., Kim, B. In June 2024 (Published) ArXiv BibTeX
Robust Machine Learning Conference Paper Effective pruning of web-scale datasets based on complexity of concept clusters Abbas, A., Rusak, E., Tirumala, K., Brendel, W., Chaudhuri, K., Morcos, A. S. In January 2024 (Published) ArXiv BibTeX
Robust Machine Learning Conference Paper InfoNCE: Identifying the Gap Between Theory and Practice Rusak, E., Reizinger, P., Juhos, A., Bringmann, O., Zimmermann, R. S., Brendel, W. In July 2024 (Published) BibTeX
Robust Machine Learning Conference Paper Measuring Per-Unit Interpretability at Scale Without Humans Klindt, D., Zimmermann, R., Brendel, W. In September 2024 (Published) OpenReview BibTeX
Robust Machine Learning Article Translational symmetry in convolutions with localized kernels causes an implicit bias toward high frequency adversarial examples Caro, J. O., Ju, Y., Pyle, R., Dey, S., Brendel, W., Anselmi, F., Patel, A. B. Frontiers in Computational Neuroscience, 18:1387077, June 2024 (Published) Frontiers in Computational Neuroscience BibTeX
Robust Machine Learning Conference Paper Scale Alone Does not Improve Mechanistic Interpretability in Vision Models Zimmermann, R. S., Klein, T., Brendel, W. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), :57876 - 57907, Curran Associates Inc., NeurIPS, October 2023 (Published) NeurIPS Proceedings DOI URL BibTeX
Empirical Inference Robust Machine Learning Conference Paper Desiderata for Representation Learning from Identifiability, Disentanglement, and Group-Structuredness Keurti, H., Reizinger, P., Schölkopf, B., Brendel, W. 2nd Annual Topology, Algebra, and Geometry in Machine Learning (TAG) at ICML 2023, July 2023 (Published) URL BibTeX
Empirical Inference Robust Machine Learning Conference Paper Provably Learning Object-Centric Representations Brady*, J., Zimmermann*, R. S., Sharma, Y., Schölkopf, B., von Kügelen, J., Brendel, W. Proceedings of the 40th International Conference on Machine Learning (ICML), 202:3038-3062, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), JMLR, Cambridge, MA, July 2023, *equal contribution (Published) URL BibTeX
Empirical Inference Robust Machine Learning Article Jacobian-based Causal Discovery with Nonlinear ICA Reizinger, P., Sharma, Y., Bethge, M., Schölkopf, B., Huszár, F., Brendel, W. Transactions on Machine Learning Research, April 2023 (Published) URL BibTeX
Robust Machine Learning Conference Paper Compositional Generalization from First Principles Wiedemer, T., Mayilvahanan, P., Bethge, M., Brendel, W. In July 2023 (Published) NeurIPS Proceedings BibTeX
Robust Machine Learning Conference Paper Provable Compositional Generalization for Object-Centric Learning Wiedemer, T., Brady, J., Panfilov, A., Juhos, A., Bethge, M., Brendel, W. In October 2023 (Published) ArXiv BibTeX
Empirical Inference Autonomous Learning Robust Machine Learning Conference Paper Embrace the Gap: VAEs Perform Independent Mechanism Analysis Reizinger*, P., Gresele*, L., Brady*, J., von Kügelgen, J., Zietlow, D., Schölkopf, B., Martius, G., Brendel, W., Besserve, M. Advances in Neural Information Processing Systems (NeurIPS 2022), 35:12040-12057, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022, *equal first authorship (Published) Arxiv PDF URL BibTeX