Empirical Inference


2024


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Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips

Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J.

Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (misc) Accepted

Abstract
Beyond vision and hearing, tactile sensing enhances a robot's ability to dexterously manipulate unfamiliar objects and safely interact with humans. Giving touch sensitivity to robots requires compact, robust, affordable, and efficient hardware designs, especially for high-resolution tactile sensing. We present a soft vision-based tactile sensor engineered to meet these requirements. Comparable in size to a human fingertip, Minsight uses machine learning to output high-resolution directional contact force distributions at 60 Hz. Minsight's tactile force maps enable precise sensing of fingertip contacts, which we use in this hands-on demonstration to allow a 3-DoF robot arm to physically track contact with a user's finger. While observing the colorful image captured by Minsight's internal camera, attendees can experience how its ability to detect delicate touches in all directions facilitates real-time robot interaction.

Project Page [BibTex]

2024

Project Page [BibTex]


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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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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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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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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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Language Models Can Reduce Asymmetry in Information Markets

Rahaman, N., Weiss, M., Wüthrich, M., Bengio, Y., Li, E., Pal, C., Schölkopf, B.

arXiv:2403.14443, March 2024, Published as: Redesigning Information Markets in the Era of Language Models, Conference on Language Modeling (COLM) (techreport)

Abstract
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.

link (url) [BibTex]

link (url) [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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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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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.

2024 (misc) Submitted

arXiv [BibTex]

arXiv [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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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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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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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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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]


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Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases

Blum, D., Hepp, T., Belov, V., Goya-Maldonado, R., la Fougère, C., Reimold, M.

Human Brain Mapping, 44(7):2802-2814, May 2023 (article)

DOI [BibTex]

DOI [BibTex]


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Staying and Returning dynamics of young children’s attention

Kim, J., Singh, S., Vales, C., Keebler, E., Fisher, A. V., Thiessen, E. D.

Developmental Science, 26(6), May 2023 (article)

DOI [BibTex]

DOI [BibTex]


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Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis

Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez-Villegas, J. F., Logothetis, N., Besserve, M.

PLOS Computational Biology, 19(4):45, Public Library of Science, April 2023 (article)

bioRxiv DOI Project Page [BibTex]

bioRxiv DOI Project Page [BibTex]


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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 (article)

link (url) [BibTex]

link (url) [BibTex]


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The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

Schreiber*, J., Boix*, C., Lee, J. W., Li, H., Guan, Y., Chang, C., Chang, J., Hawkins-Hooker, A., Schölkopf, B., Schweikert, G., Carulla, M. R., Canakoglu, A., Guzzo, F., Nanni, L., Masseroli, M., Carman, M. J., Pinoli, P., Hong, C., Yip, K. Y., Spence, J. P., Batra, S. S., Song, Y. S., Mahony, S., Zhang, Z., Tan, W., Shen, Y., Sun, Y., Shi, M., Adrian, J., Sandstrom, R., Farrell, N., Halow, J., Lee, K., Jiang, L., Yang, X., Epstein, C., Strattan, J. S., Bernstein, B., Snyder, M., Kellis, M., Stafford, W., Kundaje, A., ENCODE Imputation Challenge Participants,

Genome Biology, 24, April 2023, *co‑first authors (article)

DOI [BibTex]

DOI [BibTex]