Publications

Empirical Inference Conference Paper Some Intriguing Aspects about Lipschitz Continuity of Neural Networks Khromov*, G., Singh*, S. P. The Twelfth International Conference on Learning Representations (ICLR), May 2024, *equal contribution (Published) arXiv BibTeX

Empirical Inference Conference Paper Stochastic Gradient Descent for Gaussian Processes Done Right Lin*, J. A., Padhy*, S., Antorán*, J., Tripp, A., Terenin, A., Szepesvari, C., Hernández-Lobato, J. M., Janz, D. The Twelfth International Conference on Learning Representations (ICLR), May 2024, *equal contribution (Published) arXiv BibTeX

Empirical Inference Conference Paper Targeted Reduction of Causal Models Kekić, A., Schölkopf, B., Besserve, M. ICLR 2024 Workshop on AI4DifferentialEquations In Science, May 2024 (Published) URL BibTeX

Empirical Inference Autonomous Learning Conference Paper The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks Spieler, A., Rahaman, N., Martius, G., Schölkopf, B., Levina, A. In The Twelfth International Conference on Learning Representations (ICLR), May 2024 (Published) arXiv BibTeX

Empirical Inference Conference Paper Towards Meta-Pruning via Optimal Transport Theus, A., Geimer, O., Wicke, F., Hofmann, T., Anagnostidis, S., Singh, S. P. The Twelfth International Conference on Learning Representations (ICLR), May 2024 (Published) arXiv BibTeX

Empirical Inference Conference Paper Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion Meterez*, A., Joudaki*, A., Orabona, F., Immer, A., Rätsch, G., Daneshmand, H. The Twelfth International Conference on Learning Representations (ICLR), May 2024, *equal contribution (Published) arXiv BibTeX

Empirical Inference Conference Paper Transformer Fusion with Optimal Transport Imfeld*, M., Graldi*, J., Giordano*, M., Hofmann, T., Anagnostidis, S., Singh, S. P. The Twelfth International Conference on Learning Representations (ICLR), May 2024, *equal contribution (Published) arXiv BibTeX

Empirical Inference Master Thesis Algorithmic Compositional Learning of Language Models Thomm, J. ETH Zurich, Switzerland, April 2024 (Published) BibTeX

Empirical Inference Miscellaneous Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA Gupte, N., Ramos-Buades, A., Buonanno, A., Gair, J., Miller, M. C., Dax, M., Green, S. R., Pürrer, M., Wildberger, J., Macke, J. H., Romero-Shaw, I. M., Schölkopf, B. April 2024 (Published) URL BibTeX

Empirical Inference Conference Paper PILLAR: How to make semi-private learning more effective Pinto, F., Hu, Y., Yang, F., Sanyal, A. 2nd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 110-139, April 2024 (Published) DOI BibTeX

Empirical Inference Article SimReadUntil for benchmarking selective sequencing algorithms on ONT devices Mordig, M., Ratsch, G., Kahles, A. Bioinformatics, 40(5):btae199, April 2024 (Published) DOI URL BibTeX

Empirical Inference Article VIPurPCA: Visualizing and Propagating Uncertainty in Principal Component Analysis Zabel, S., Hennig, P., Nieselt, K. IEEE Transactions on Visualization and Computer Graphics, 30(4):2011-2022, April 2024 (Published) DOI BibTeX

Empirical Inference Poster Koopman Spectral Analysis Uncovers the Temporal Structure of Spontaneous Neural Events Shao, K., Xu, Y., Logothetis, N., Shen, Z., Besserve, M. Computational and Systems Neuroscience Meeting (COSYNE), March 2024 (Published) URL BibTeX

Empirical Inference Master Thesis Interpreting How Large Language Models Handle Facts and Counterfactuals through Mechanistic Interpretability Ortu, F. University of Trieste, Italy, March 2024 (Published) BibTeX

Empirical Inference Article Learning Graph Embeddings for Open World Compositional Zero-Shot Learning Mancini, M., Naeem, M. F., Xian, Y., Akata, Z. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(3):1545-1560, IEEE, New York, NY, March 2024 (Published) DOI BibTeX

Empirical Inference Poster LFP transient events in macaque subcortical areas reveal network coordination across scales and structures: a simultaneous fMRI-electrophysiology study Besserve, M., Safavi, S., Schölkopf, B., Logothetis, N. Computational and Systems Neuroscience Meeting (COSYNE), March 2024 (Published) URL BibTeX

Empirical Inference Ph.D. Thesis Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment von Kügelgen, J. University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (Published) URL BibTeX

Empirical Inference Conference Paper Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling Gebhard, T. D., Wildberger, J., Dax, M., Angerhausen, D., Quanz, S. P., Schölkopf, B. 3rd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), February 2024 (Published) arXiv URL BibTeX

Empirical Inference Ph.D. Thesis Methods for Generative Modeling and Interpretable Classification with Strong Differential Privacy Harder, F. University of Tübingen, Germany, February 2024, IMPRS-PhD-Program (Published) URL BibTeX

Empirical Inference Article Trained recurrent neural networks develop phase-locked limit cycles in a working memory task Pals, M., Macke, J. H., Barak, O. PLOS Computational Biology, 20(2), February 2024 (Published) DOI BibTeX

Empirical Inference Conference Paper Multi-channel free space optical convolutions with incoherent light Song, A., Kottapalli, S. N. M., Schölkopf, B., Fischer, P. AI and Optical Data Sciences V, PC12903:PC129030I, (Editors: Ken-ichi Kitayama and Volker J. Sorger), SPIE, January 2024 (Published) DOI BibTeX

Empirical Inference Article Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery Visonà, G., Bouzigon, E., Demenais, F., Schweikert, G. Briefings in Bioinformatics, 25(2), January 2024 (Published) DOI BibTeX

Empirical Inference Optics and Sensing Laboratory Conference Paper Polarization-based non-linear deep diffractive neural networks Kottapalli, S. N. M., Schlieder, L., Song, A., Volchkov, V., Schölkopf, B., Fischer, P. AI and Optical Data Sciences V, PC12903:PC129030B, (Editors: Ken-ichi Kitayama and Volker J. Sorger), SPIE, January 2024 (Published) DOI BibTeX

Empirical Inference Article Pre-treatment 18F-FDG-PET/CT parameters as biomarkers for progression free survival, best overall response and overall survival in metastatic melanoma patients undergoing first-line immunotherapy Peisen, F., Gerken, A., Dahm, I., Nikolaou, K., Eigentler, T., Amaral, T., Moltz, J. H., Othman, A. E., Gatidis, S. PLOS ONE, 19(1), January 2024 (Published) DOI BibTeX

Empirical Inference Article Towards fully covariant machine learning Villar, S., Hogg, D. W., Yao, W., Kevrekidis, G. A., Schölkopf, B. Transactions on Machine Learning Research, January 2024 (Published) URL BibTeX

Empirical Inference Miscellaneous Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models Rajendran, G., Buchholz, S., Aragam, B., Schölkopf, B., Ravikumar, P. 2024 (Published) arXiv BibTeX

Empirical Inference Miscellaneous Multi-Armed Bandits and Quantum Channel Oracles Buchholz, S., Kübler, J. M., Schölkopf, B. 2024 (Published) arXiv BibTeX

Empirical Inference Article Optimal Decision Making Under Strategic Behavior Tsirtsis, S., Tabibian, B., Khajehnejad, M., Singla, A., Schölkopf, B., Gomez-Rodriguez, M. Management Science, 2024, Published Online (In press) DOI BibTeX

Empirical Inference Article Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks Gebhard, T. D., Angerhausen, D., Konrad, B. S., Alei, E., Quanz, S. P., Schölkopf, B. Astronomy & Astrophysics, 681, 2024 (Published) DOI BibTeX

Empirical Inference Article Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011 Bonse, M. J., Gebhard, T. D., Dannert, F. A., Absil, O., Cantalloube, F., Christiaens, V., Cugno, G., Garvin, E. O., Hayoz, J., Kasper, M., Matthews, E., Schölkopf, B., Quanz, S. P. The Astronomical Journal, 2024 (Accepted) arXiv BibTeX

Empirical Inference Article A phenomenological account for causality in terms of elementary actions Janzing, D., Garrido Mejia, S. Journal of Causal Inference, 12(1), 2024 (Published) DOI URL BibTeX

Empirical Inference Article A temperate super-Jupiter imaged with JWST in the mid-infrared Matthews, E. C., Carter, A. L., Pathak, P., Morley, C. V., Phillips, M. W., S. Krishanth, P. M., Feng, F., Bonse, M. J., Boogaard, L. A., Burt, J. A., Crossfield, I. J. M., Douglas, E. S., Henning, T., Hom, J., Ko, C. -., Kasper, M., Lagrange, A., Petit Dit de la Roche, D., Philipot, F. Nature, 633:789–792, 2024 (Published)

Abstract ›

Of the approximately 25 directly imaged planets to date, all are younger than 500 Myr, and all but six are younger than 100 Myr (ref. 1). Eps Ind A (HD209100, HIP108870) is a K5V star of roughly solar age (recently derived as 3.7–5.7 Gyr (ref. 2) and Gyr (ref. 3)). A long-term radial-velocity trend4,5 and an astrometric acceleration6,7 led to claims of a giant planet2,8,9 orbiting the nearby star (3.6384 ± 0.0013 pc; ref. 10). Here we report JWST coronagraphic images which reveal a giant exoplanet that is consistent with these radial and astrometric measurements but inconsistent with the previously claimed planet properties. The new planet has a temperature of approximately 275 K and is remarkably bright at 10.65 and 15.50 µm. Non-detections between 3.5 and 5.0 µm indicate an unknown opacity source in the atmosphere, possibly suggesting a high-metallicity, high carbon-to-oxygen ratio planet. The best-fitting temperature of the planet is consistent with theoretical thermal evolution models, which were previously untested at this temperature range. The data indicate that this is probably the only giant planet in the system, and therefore we refer to it as b, despite it having significantly different orbital properties than the previously claimed planet b.

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Empirical Inference Miscellaneous Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries Ceraolo, R., Kharlapenko, D., Khan, A., Reymond, A., Mihalcea, R., Sachan, M., Schölkopf, B., Jin, Z. 2024 (Published) URL BibTeX

Empirical Inference Article Connectome-constrained networks predict neural activity across the fly visual system Lappalainen, J. K., Tschopp, F. D., Prakhya, S., McGill, M., Nern, A., Shinomiya, K., Takemura, S., Gruntman, E., Macke, J. H., Turaga, S. C. Nature, 634:1132–1140, 2024 (Published)

Abstract ›

We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question10. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.

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Empirical Inference Conference Paper DeViL: Decoding Vision features into Language Dani, M., Rio-Torto, I., Alaniz, S., Akata, Z. In Lecture Notes in Computer Science, vol 14264, 363–377, 45th Annual Conference of the German-Association-for-Pattern-Recognition (DAGM GCPR), 2024 (Published) DOI URL BibTeX

Empirical Inference Miscellaneous Language Model Alignment in Multilingual Trolley Problems Jin, Z., Levine, S., Kleiman-Weiner, M., Piatti, G., Liu, J., Gonzalez, F., Ortu, F., Strausz, A., Sachan, M., Mihalcea, R., Choi, Y., Schölkopf, B. 2024 (Published) URL BibTeX

Empirical Inference Article Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light Song, A., Kottapalli, S. N. M., Goyal, R., Schoelkopf, B., Fischer, P. Nature Communications, 15:10692, 2024 (Published)

Abstract ›

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy.

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Empirical Inference Article Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series Vetter, J., Lim, K., Dijkstra, T. M. H., Dargaville, P. A., Kohlbacher, O., Macke, J. H., Poets, C. F. PLOS Health Digital, 3(12):e0000678, 2024 (Accepted) DOI URL BibTeX

Empirical Inference Conference Paper Waffling around for Performance: Visual Classification with Random Words and Broad Concepts Roth, K., Kim, J. M., Koepke, A. S., Vinyals, O., Schmid, C., Akata, Z. In Proceedings 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 15700-15711, 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2024 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Optimistic Active Exploration of Dynamical Systems Sukhija, B., Treven, L., Sancaktar, C., Blaes, S., Coros, S., Krause, A. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 1-32, Curran Associates Inc. , NeurIPS, December 2023 (Published) DOI URL BibTeX

Empirical Inference Article Machine-Learning-Aided Prediction of Brain Metastases Development in Non-Small-Cell Lung Cancers Visonà, G., Spiller, L. M., Hahn, S., Hattingen, E., Vogl, T. J., Schweikert, G., Bankov, K., Demes, M., Reis, H., Wild, P., Zeiner, P. S., Acker, F., Sebastian, M., Wenger, K. J. Clinical lung cancer, 24(8):e311-e322, December 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper A Measure-Theoretic Axiomatisation of Causality Park, J., Buchholz, S., Schölkopf, B., Muandet, K. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:28510-28540, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Conference Paper Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good Gonzalez*, F., Jin*, Z., Schölkopf, B., Hope, T., Sachan, M., Mihalcea, R. Findings of the Association for Computational Linguistics: EMNLP 2023, 415-438, (Editors: Houda Bouamor and Juan Pino and Kalika Bali), Association for Computational Linguistics, December 2023, *equal contribution (Published) DOI BibTeX

Empirical Inference Conference Paper CLadder: Assessing Causal Reasoning in Language Models Jin*, Z., Chen*, Y., Leeb*, F., Gresele*, L., Kamal, O., Lyu, Z., Blin, K., Gonzalez, F., Kleiman-Weiner, M., Sachan, M., Schölkopf, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:31038-31065, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *main contributors (Published) URL BibTeX

Empirical Inference Conference Paper Can semi-supervised learning use all the data effectively? A lower bound perspective Tifrea*, A., Yüce*, G., Sanyal, A., Yang, F. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:21960-21982, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Causal Component Analysis Liang, W., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele*, L., Schölkopf*, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:32481-32520, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *shared last author (Published) URL BibTeX

Empirical Inference Conference Paper Causal Modeling with Stationary Diffusions Lorch, L., Krause*, A., Schölkopf*, B. Causal Representation Learning Workshop at NeurIPS 2023, December 2023, *equal supervision (Published) URL BibTeX

Empirical Inference Conference Paper Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data Guo*, S., Tóth*, V., Schölkopf, B., Huszár, F. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:36463-36475, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Causal normalizing flows: from theory to practice Javaloy, A., Sanchez-Martin, P., Valera, I. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:58833-58864, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX

Empirical Inference Perceiving Systems Conference Paper Controlling Text-to-Image Diffusion by Orthogonal Finetuning Qiu*, Z., Liu*, W., Feng, H., Xue, Y., Feng, Y., Liu, Z., Zhang, D., Weller, A., Schölkopf, B. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:79320-79362, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems , December 2023, *equal contribution (Published)

Abstract ›

Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models to perform different downstream tasks becomes an important open problem. To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks. Unlike existing methods, OFT can provably preserve hyperspherical energy which characterizes the pairwise neuron relationship on the unit hypersphere. We find that this property is crucial for preserving the semantic generation ability of text-to-image diffusion models. To improve finetuning stability, we further propose Constrained Orthogonal Finetuning (COFT) which imposes an additional radius constraint to the hypersphere. Specifically, we consider two important finetuning text-to-image tasks: subject-driven generation where the goal is to generate subject-specific images given a few images of a subject and a text prompt, and controllable generation where the goal is to enable the model to take in additional control signals. We empirically show that our OFT framework outperforms existing methods in generation quality and convergence speed.

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