Publications

Empirical Inference Article In silico biological discovery with large perturbation models Miladinovic*, D., Höppe*, T., Chevalley, M., Georgiou, A., Stuart, L., Mehrjou, A., Bantscheff, M., Schölkopf, B., Schwab, P. Nature Computational Science, October 2025, *equal contribution (Published)

Abstract ›

Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks—from understanding the relationships between biological entities to developing therapeutics. However, these data encompass diverse perturbations and readouts, and the complex dependence of experimental outcomes on their biological context makes it challenging to integrate insights across experiments. Here we present the large perturbation model (LPM), a deep-learning model that integrates multiple, heterogeneous perturbation experiments by representing perturbation, readout and context as disentangled dimensions. LPM outperforms existing methods across multiple biological discovery tasks, including in predicting post-perturbation transcriptomes of unseen experiments, identifying shared molecular mechanisms of action between chemical and genetic perturbations, and facilitating the inference of gene–gene interaction networks. LPM learns meaningful joint representations of perturbations, readouts and contexts, enables the study of biological relationships in silico and could considerably accelerate the derivation of insights from pooled perturbation experiments.

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Empirical Inference Article Flow annealed importance sampling bootstrap meets differentiable particle physics Kofler, A., Stimper, V., Mikhasenko, M., Kagan, M., Heinrich, L. Machine Learning: Science and Technology, 6(2), IOP Publishing, June 2025 (Published)

Abstract ›

High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.

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Empirical Inference Article The Fiction Machine Bottou, L., Schölkopf, B. SIAM News, 58(3), April 2025 (Published) URL BibTeX

Empirical Inference Article Early warning of complex climate risk with integrated artificial intelligence Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creutzig, F., Fearnley, C. J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., Weldemariam, K. Nature Communications, 16(1), March 2025 (Published) DOI BibTeX

Empirical Inference Article Real-time inference for binary neutron star mergers using machine learning Dax, M., Green, S. R., Gair, J., Gupte, N., Pürrer, M., Raymond, V., Wildberger, J., Macke, J. H., Buonanno, A., Schölkopf, B. Nature, 639(8053):49-53, March 2025 (Published) DOI URL BibTeX

Empirical Inference Article Artificial intelligence for modelling infectious disease epidemics Kraemer, M. U. G., Tsui, J. L., Chang, S. Y., Lytras, S., Khurana, M. P., Vanderslott, S., Bajaj, S., Scheidwasser, N., Curran-Sebastian, J. L., Semenova, E., Zhang, M., Unwin, H. J. T., Watson, O. J., Mills, C., Dasgupta, A., Ferretti, L., Scarpino, S. V., Koua, E., Morgan, O., Tegally, H., et al. Nature, 638(8051):623-635, February 2025 (Published) DOI URL BibTeX

Empirical Inference Article Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels Gebhard, T. D., Wildberger, J., Dax, M., Kofler, A., Angerhausen, D., Quanz, S., Schölkopf, B. Astronomy \& Astrophysics, 693, January 2025 (Published) DOI URL BibTeX

Empirical Inference Article A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions Rastogi, C., Song, X., Jin, Z., Stelmakh, I., Daumé III, H., Zhang, K., Shah, N. B. PLOS ONE, 19(12), Public Library of Science, December 2024 (Published) DOI URL BibTeX

Empirical Inference Article A Probabilistic Model behind Self-Supervised Learning Bizeul, A., Schölkopf, B., Allen, C. Transactions on Machine Learning Research, October 2024 (Published) PDF URL BibTeX

Empirical Inference Article How developments in natural language processing help us in understanding human behaviour Mihalcea, R., Biester, L., Boyd, R. L., Jin, Z., Perez-Rosas, V., Wilson, S., Pennebaker, J. W. Nature Human Behaviour, 8(10):1877-1889, Nature Publishing Group UK London, October 2024 (Published) DOI URL BibTeX

Haptic Intelligence Empirical Inference Optics and Sensing Laboratory Software Workshop Article Fiber-Optic Shape Sensing Using Neural Networks Operating on Multispecklegrams Cao, C. G. L., Javot, B., Bhattarai, S., Bierig, K., Oreshnikov, I., Volchkov, V. V. IEEE Sensors Journal, 24(17):27532-27540, September 2024 (Published)

Abstract ›

Application of machine learning techniques on fiber speckle images to infer fiber deformation allows the use of an unmodified multimode fiber to act as a shape sensor. This approach eliminates the need for complex fiber design or construction (e.g., Bragg gratings and time-of-flight). Prior work in shape determination using neural networks trained on a finite number of possible fiber shapes (formulated as a classification task), or trained on a few continuous degrees of freedom, has been limited to reconstruction of fiber shapes only one bend at a time. Furthermore, generalization to shapes that were not used in training is challenging. Our innovative approach improves generalization capabilities, using computer vision-assisted parameterization of the actual fiber shape to provide a ground truth, and multiple specklegrams per fiber shape obtained by controlling the input field. Results from experimenting with several neural network architectures, shape parameterization, number of inputs, and specklegram resolution show that fiber shapes with multiple bends can be accurately predicted. Our approach is able to generalize to new shapes that were not in the training set. This approach of end-to-end training on parameterized ground truth opens new avenues for fiber-optic sensor applications. We publish the datasets used for training and validation, as well as an out-of-distribution (OOD) test set, and encourage interested readers to access these datasets for their own model development.

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Empirical Inference Article Leveraging Task Structures for Improved Identifiability in Neural Network Representations Chen*, W., Horwood*, J., Heo, J., Hernández-Lobato, J. M. Transactions on Machine Learning Research, August 2024, *equal contribution (Published) URL BibTeX

Learning and Dynamical Systems Empirical Inference Article Deep Backtracking Counterfactuals for Causally Compliant Explanations Kladny, K., Kügelgen, J. V., Schölkopf, B., Muehlebach, M. Transactions on Machine Learning Research, July 2024 (Published) arXiv URL BibTeX

Empirical Inference Article Probabilistic pathway-based multimodal factor analysis Immer, A., Stark, S. G., Jacob, F., Bonilla, X., Thomas, T., Kahles, A., Goetze, S., Milani, E. S., Wollscheid, B., Consortium, T. T. P., Rätsch, G., Lehmann, K. Bioinformatics, 40(Supplement 1):i189-i198, July 2024 (Published) DOI URL BibTeX

Empirical Inference Article Grundfragen der künstlichen Intelligenz Schölkopf, B. astronomie - Das Magazin, 42, May 2024 (Published) URL 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 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 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 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 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 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 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 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 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 Article Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information Visonà, G., Duroux, D., Miranda, L., Sükei, E., Li, Y., Borgwardt, K., Oliver, C. Bioinformatics, 39(12), December 2023 (Published) DOI BibTeX

Empirical Inference Article Data-Efficient Learning via Minimizing Hyperspherical Energy Cao, X., Liu, W., Tsang, I. W. IEEE transactions on pattern analysis and machine intelligence, 45(11):13422-13437, November 2023 (Published) DOI BibTeX

Empirical Inference Article Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population Kerber, B., Hepp, T., Küstner, T., Gatidis, S. PLOS ONE, 18(11), November 2023 (Published) DOI BibTeX

Empirical Inference Article Kernel-Based Independence Tests for Causal Structure Learning on Functional Data Laumann, F., von Kügelgen, J., Park, J., Schölkopf, B., Barahona, M. Entropy, 25(12), November 2023 (Published) DOI BibTeX

Empirical Inference Article Variational Causal Dynamics: Discovering Modular World Models from Interventions Lei, A., Schölkopf, B., Posner, I. Transactions on Machine Learning Research, November 2023 (Published) URL BibTeX

Empirical Inference Article A taxonomy and review of generalization research in NLP Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., Jin, Z. Nature Machine Intelligence, 5(10):1161-1174, October 2023 (Published) DOI BibTeX

Empirical Inference Article Artificial Intelligence in Oncological Hybrid Imaging Feuerecker, B., Heimer, M. M., Geyer, T., Fabritius, M. P., Gu, S., Schachtner, B., Beyer, L., Ricke, J., Gatidis, S., Ingrisch, M., Cyran, C. C. Nuklearmedizin, 62(5):296-305, October 2023 (Published) DOI BibTeX

Empirical Inference Article Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study Peisen, F., Gerken, A., Dahm, I., Nikolaou, K., Eigentler, T., Amaral, T., Moltz, J. H., Othman, A. E., Gatidis, S., Dondi, F. Diagnostics (Basel), 13(20), October 2023 (Published) DOI BibTeX

Empirical Inference Article CROCODILE - Incorporating medium-resolution spectroscopy of close-in directly imaged exoplanets into atmospheric retrievals via cross-correlation Hayoz, J., Cugno, G., Quanz, S. P., Patapis, P., Alei, E., Bonse, M. J., Dannert, F. A., Garvin, E. O., Gebhard, T. D., Konrad, B. S., Sartori, L. F. Astronomy & Astrophysics, 678, October 2023 (Published) DOI BibTeX

Empirical Inference Article A historical perspective of biomedical explainable AI research Malinverno, L., Barros, V., Ghisoni, F., Visonà, G., Kern, R., Nickel, P. J., Ventura, B. E., Šimić, I., Stryeck, S., Manni, F., Ferri, C., Jean-Quartier, C., Genga, L., Schweikert, G., Lovrić, M., Rosen-Zvi, M. Patterns, 4(9), September 2023 (Published) DOI BibTeX

Empirical Inference Article Neural Causal Structure Discovery from Interventions Ke*, N. R., Bilaniuk*, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y. Transactions on Machine Learning Research, September 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Article Simulation-based inference for efficient identification of generative models in computational connectomics Boelts, J., Harth, P., Gao, R., Udvary, D., Yáñez, F., Baum, D., Hege, H., Oberlaender, M., Macke, J. H. PLOS Computational Biology, 19(9):1-28, September 2023 (Published) DOI BibTeX

Empirical Inference Article Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes Buchholz, S. Transactions on Machine Learning Research, September 2023 (Published) URL BibTeX

Empirical Inference Article Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory Vaughan, S. R., Gebhard, T. D., Bott, K., Casewell, S. L., Cowan, N. B., Doelman, D. S., Kenworthy, M., Mazoyer, J., Millar-Blanchaer, M. A., Trees, V. J. H., Stam, D. M., Absil, O., Altinier, L., Baudoz, P., Belikov, R., Bidot, A., Birkby, J. L., Bonse, M. J., Brandl, B., Carlotti, A., et al. Monthly Notices of the Royal Astronomical Society, 524(4):5477-5485, August 2023 (Published) DOI BibTeX

Empirical Inference Article Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning Hawkins-Hooker, A., Visonà, G., Narendra, T., Rojas-Carulla, M., Schölkopf, B., Schweikert, G. Nature Communications, 14(1), August 2023 (Published) DOI BibTeX

Haptic Intelligence Autonomous Learning Empirical Inference Article Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation Andrussow, I., Sun, H., Kuchenbecker, K. J., Martius, G. Advanced Intelligent Systems, 5(8):2300042, August 2023, Inside back cover, DOI: 10.1002/aisy.202370035 (Published)

Abstract ›

Intelligent interaction with the physical world requires perceptual abilities beyond vision and hearing; vibrant tactile sensing is essential for autonomous robots to dexterously manipulate unfamiliar objects or safely contact humans. Therefore, robotic manipulators need high-resolution touch sensors that are compact, robust, inexpensive, and efficient. The soft vision-based haptic sensor presented herein is a miniaturized and optimized version of the previously published sensor Insight. Minsight has the size and shape of a human fingertip and uses machine learning methods to output high-resolution maps of 3D contact force vectors at 60 Hz. Experiments confirm its excellent sensing performance, with a mean absolute force error of 0.07 N and contact location error of 0.6 mm across its surface area. Minsight's utility is shown in two robotic tasks on a 3-DoF manipulator. First, closed-loop force control enables the robot to track the movements of a human finger based only on tactile data. Second, the informative value of the sensor output is shown by detecting whether a hard lump is embedded within a soft elastomer with an accuracy of 98\%. These findings indicate that Minsight can give robots the detailed fingertip touch sensing needed for dexterous manipulation and physical human–robot interaction.

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Empirical Inference Article A network approach to atomic spectra Wellnitz, D., Kekić, A., Heiss, J., Gertz, M., Weidemüller, M., Spitz, A. Journal of Physics: Complexity, 4(3), July 2023 (Published) DOI BibTeX

Empirical Inference Article Catastrophic overfitting can be induced with discriminative non-robust features Ortiz-Jimenez*, G., de Jorge*, P., Sanyal, A., Bibi, A., Dokania, P. K., Frossard, P., Rogez, G., Torr, P. Transactions on Machine Learning Research , July 2023, *equal contribution (Published) PDF Code URL BibTeX

Empirical Inference Article Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-contrast Imaging in the Presence of Non-Gaussian Noise Bonse, M. J., Garvin, E. O., Gebhard, T. D., Dannert, F. A., Cantalloube, F., Cugno, G., Absil, O., Hayoz, J., Milli, J., Kasper, M., Quanz, S. P. The American Astronomical Society, 166(2), July 2023 (Published) DOI BibTeX

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