Empirical Inference


2024


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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 (article)

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.

DOI [BibTex]


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Advances in Probabilistic Methods for Deep Learning

Immer, A.

ETH Zurich, Switzerland, September 2024, CLS PhD Program (phdthesis)

[BibTex]

[BibTex]


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A Probabilistic Model behind Self-Supervised Learning

Bizeul, A., Schölkopf, B., Allen, C.

Transactions on Machine Learning Research, September 2024 (article) To be published

PDF [BibTex]

PDF [BibTex]


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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 (article)

link (url) [BibTex]

link (url) [BibTex]


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A Measure-Theoretic Axiomatisation of Causality and Kernel Regression

Park, J.

University of Tübingen, Germany, July 2024 (phdthesis)

[BibTex]

[BibTex]


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Deep Backtracking Counterfactuals for Causally Compliant Explanations

Kladny, K., Kügelgen, J. V., Schölkopf, B., Muehlebach, M.

Transactions on Machine Learning Research, July 2024 (article)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Advancing Normalising Flows to Model Boltzmann Distributions

Stimper, V.

University of Cambridge, UK, Cambridge, June 2024, (Cambridge-Tübingen-Fellowship-Program) (phdthesis)

[BibTex]

[BibTex]


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Grundfragen der künstlichen Intelligenz

Schölkopf, B.

astronomie - Das Magazin, 42, May 2024 (article)

link (url) [BibTex]

link (url) [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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Identifiable Causal Representation Learning

von Kügelgen, J.

University of Cambridge, UK, Cambridge, February 2024, (Cambridge-Tübingen-Fellowship) (phdthesis)

[BibTex]

[BibTex]


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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), February 2024 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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Towards fully covariant machine learning

Villar, S., Hogg, D. W., Yao, W., Kevrekidis, G. A., Schölkopf, B.

Transactions on Machine Learning Research, January 2024 (article)

link (url) [BibTex]

link (url) [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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Optimal Decision Making Under Strategic Behavior

Tsirtsis, S., Tabibian, B., Khajehnejad, M., Singla, A., Schölkopf, B., Gomez-Rodriguez, M.

Management Science, 2024, Published Online (article) In press

DOI [BibTex]

DOI [BibTex]


SimReadUntil for benchmarking selective sequencing algorithms on ONT devices
SimReadUntil for benchmarking selective sequencing algorithms on ONT devices

Mordig, M., Ratsch, G., Kahles, A.

Bioinformatics, 40, 2024 (article)

[BibTex]

[BibTex]

2023


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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 (article)

DOI [BibTex]

2023

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures

Jenny, D.

ETH Zurich, Switzerland, November 2023, external supervision (thesis)

[BibTex]

[BibTex]


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Variational Causal Dynamics: Discovering Modular World Models from Interventions

Lei, A., Schölkopf, B., Posner, I.

Transactions on Machine Learning Research, November 2023 (article)

link (url) [BibTex]

link (url) [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

link (url) [BibTex]

link (url) [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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., Choquet, E., van Dam, D., Desai, N., Fogarty, K., Fowler, J., van Gorkom, K., Gutierrez, Y., Guyon, O., Haffert, S. Y., Herscovici-Schiller, O., Hours, A., Juanola-Parramon, R., Kleisioti, E., König, L., van Kooten, M., Krasteva, M., Laginja, I., Landman, R., Leboulleux, L., Mouillet, D., N’Diaye, M., Por, E. H., Pueyo, L., Snik, F.

Monthly Notices of the Royal Astronomical Society, 524(4):5477-5485, August 2023 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

DOI [BibTex]

DOI [BibTex]


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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 (article)

PDF Code link (url) [BibTex]

PDF Code link (url) [BibTex]


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Learning and Testing Powerful Hypotheses

Kübler, J. M.

University of Tübingen, Germany, July 2023 (phdthesis)

[BibTex]

[BibTex]


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Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward-based tasks

Wang, Q., Sanchez, F. R., McCarthy, R., Bulens, D. C., McGuinness, K., O’Connor, N., Wüthrich, M., Widmaier, F., Bauer, S., Redmond, S. J.

Expert Systems, 40(6), July 2023 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning Identifiable Representations: Independent Influences and Multiple Views

Gresele, L.

University of Tübingen, Germany, June 2023 (phdthesis)

[BibTex]


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Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data

Katiyar, P., Schwenck, J., Frauenfeld, L., Divine, M. R., Agrawal, V., Kohlhofer, U., Gatidis, S., Kontermann, R., Königsrainer, A., Quintanilla-Martinez, L., la Fougère, C., Schölkopf, B., Pichler, B. J., Disselhorst, J. A.

Nature Biomedical Engineering, 7(8):1014-1027, June 2023 (article)

DOI [BibTex]

DOI [BibTex]


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Classifying the unknown: Insect identification with deep hierarchical Bayesian learning

Badirli, S., Picard, C. J., Mohler, G., Richert, F., Akata, Z., Dundar, M.

Methods in Ecology and Evolution, 14(6):1515-1530, June 2023 (article)

DOI [BibTex]

DOI [BibTex]


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ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning

Mineeva*, O., Danciu*, D., Schölkopf, B., Ley, R. E., Rätsch, G., Youngblut, N. D.

PLOS Computational Biology, 19(5), Public Library of Science, San Francisco, CA, May 2023, *equal contribution (article)

DOI [BibTex]

DOI [BibTex]


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Better Together: Data Harmonization and Cross-StudAnalysis of Abdominal MRI Data From UK Biobank and the German National Cohort

Gatidis, S., Kart, T., Fischer, M., Winzeck, S., Glocker, B., Bai, W., Bülow, R., Emmel, C., Friedrich, L., Kauczor, H., Keil, T., Kröncke, T., Mayer, P., Niendorf, T., Peters, A., Pischon, T., Schaarschmidt, B., Schmidt, B., Schulze, M., Umutle, L., Völzke, H., Küstner, T., Bamberg, F., Schölkopf, B., Rueckert, D.

Investigative Radiology, 58(5):346-354, May 2023 (article)

DOI [BibTex]

DOI [BibTex]


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Learning with and for discrete optimization

Paulus, M.

ETH Zurich, Switzerland, May 2023, CLS PhD Program (phdthesis)

[BibTex]

[BibTex]


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A Kernel Stein Test for Comparing Latent Variable Models

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A.

Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(3):986-1011, May 2023 (article)

arXiv DOI [BibTex]

arXiv DOI [BibTex]