Empirical Inference


2023


no image
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]

2023

DOI [BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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, May 2023, *equal contribution (article)

DOI [BibTex]

DOI [BibTex]


no image
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):1-45, Public Library of Science, April 2023 (article)

bioRxiv DOI Project Page [BibTex]

bioRxiv DOI Project Page [BibTex]


no image
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]


no image
Adapting to noise distribution shifts in flow-based gravitational-wave inference

Wildberger, J., Dax, M., Green, S. R., Gair, J., Pürrer, M., Macke, J. H., Buonanno, A., Schölkopf, B.

Physical Review D, 107(8), April 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

Dax, M., Green, S. R., Gair, J., Pürrer, M., Wildberger, J., Macke, J. H., Buonanno, A., Schölkopf, B.

Physical Review Letters, 130(17), April 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
Instrumental variable regression via kernel maximum moment loss

Zhang, R., Imaizumi, M., Schölkopf, B., Muandet, K.

Journal of Causal Inference, 11(1), April 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
Multiplane Diffractive Acoustic Networks

Athanassiadis, A. G., Schlieder, L., Melde, K., Volchkov, V., Schölkopf, B., Fischer, P.

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 70(5):441-448, IEEE, March 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
Proactive Contact Tracing

Gupta, P., Maharaj, T., Weiss, M., Rahaman, N., Alsdurf, H., Minoyan, N., Harnois-Leblanc, S., Merckx, J., Williams, A., Schmidt, V., St-Charles, P., Patel, A., Zhang, Y., Buckeridge, D. L., Pal, C., Schölkopf, B., Bengio, Y.

PLOS Digital Health, 2(3):1-19, March 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations

Fischer, M., Hepp, T., Gatidis, S., Yang, B.

Computerized Medical Imaging and Graphics, 104, March 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
ViViT: Curvature Access Through The Generalized Gauss-Newton’s Low-Rank Structure

Dangel*, F., Tatzel*, L., Hennig, P.

Transactions on Machine Learning Research, February 2023, *equal contribution (article)

link (url) [BibTex]

link (url) [BibTex]


no image
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

Choe, J., Oh, S. J., Chun, S., Lee, S., Akata, Z., Shim, H.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):1732-1748, February 2023 (article)

DOI [BibTex]

DOI [BibTex]


no image
SphereFace Revived: Unifying Hyperspherical Face Recognition

Liu, W., Wen, Y., Raj, B., Singh, R., Weller, A.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2458-2474, February 2023 (article)

DOI [BibTex]

DOI [BibTex]


Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.

Robotics and Autonomous Systems, 159, January 2023 (article)

Abstract
High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

Arxiv Video DOI [BibTex]


no image
Learning Dynamical Systems using Local Stability Priors

Mehrjou, A., Iannelli, A., Schölkopf, B.

Journal of Computational Dynamics, 10(1):175-198, January 2023, Special issue "Computation of Lyapunov functions and contraction metrics" (article)

DOI [BibTex]

DOI [BibTex]


no image
Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases

Mehrjou*, A., Soleymani*, A., Abyaneh, A., Bhatt, S., Schölkopf, B., Bauer, S.

PLOS Computational Biology, 19(1):1-41, January 2023, *equal contribution (article)

DOI [BibTex]

DOI [BibTex]


A machine learning route between band mapping and band structure
A machine learning route between band mapping and band structure

Xian*, R. P., Stimper*, V., Zacharias, M., Dendzik, M., Dong, S., Beaulieu, S., Schölkopf, B., Wolf, M., Rettig, L., Carbogno, C., Bauer, S., Ernstorfer, R.

Nature Computational Science, 3(1):101-114, January 2023, *equal contribution (article)

arXiv DOI [BibTex]

arXiv DOI [BibTex]


no image
Natural Language Processing for Policymaking

Jin, Z., Mihalcea, R.

In Handbook of Computational Social Science for Policy, pages: 141-162, 7, (Editors: Bertoni, E. and Fontana, M. and Gabrielli, L. and Signorelli, S. and Vespe, M.), Springer International Publishing, 2023 (inbook)

DOI [BibTex]

DOI [BibTex]


no image
Information theoretic measures of causal influences during transient neural events

Shao, K., Logothetis, N. K., Besserve, M.

Frontiers in Network Physiology, 3, 2023 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


normflows: A PyTorch Package for Normalizing Flows
normflows: A PyTorch Package for Normalizing Flows

Stimper, V., Liu, D., Campbell, A., Berenz, V., Ryll, L., Schölkopf, B., Hernández-Lobato, J. M.

Journal of Open Source Software, 8(86):5361, The Journal of Open Source Software, 2023 (article)

Abstract
Normalizing flows model probability distributions through an expressive tractable density (D. Rezende & Mohamed, 2015; Esteban G. Tabak & Turner, 2013; Esteban G. Tabak & Vanden-Eijnden, 2010). They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation (Grcić et al., 2021; Kingma & Dhariwal, 2018), text modeling (Wang & Wang, 2019), variational inference (D. Rezende & Mohamed, 2015), approximating Boltzmann distributions (Noé et al., 2019), and many other problems (Kobyzev et al., 2021; Papamakarios et al., 2021). Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch (Paszke et al., 2019), which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP (Dinh et al., 2017), Glow (Kingma & Dhariwal, 2018), Masked Autoregressive Flows (Papamakarios et al., 2017), Neural Spline Flows (Durkan et al., 2019; Müller et al., 2019), Residual Flows (Chen et al., 2019), and many more. The package can be easily installed via pip and the code is publicly available on GitHub.

JOSS GitHub link (url) DOI [BibTex]

JOSS GitHub link (url) DOI [BibTex]


no image
Compact holographic sound fields enable rapid one-step assembly of matter in 3D

Melde, K., Kremer, H., Shi, M., Seneca, S., Frey, C., Platzman, I., Degel, C., Schmitt, D., Schölkopf, B., Fischer, P.

Science Advances, 9(6), 2023 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Metrizing Weak Convergence with Maximum Mean Discrepancies

Simon-Gabriel, C., Barp, A., Schölkopf, B., Mackey, L.

Journal of Machine Learning Research, 24, 2023 (article)

Abstract
This paper characterizes the maximum mean discrepancies (MMD) that metrize the weak convergence of probability measures for a wide class of kernels. More precisely, we prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, whose RKHS-functions vanish at infinity (i.e., Hk ⊂ C0), metrizes the weak convergence of probability measures if and only if k is continuous and integrally strictly positive definite (∫ s.p.d.) over all signed, finite, regular Borel measures. We also correct a prior result of Simon-Gabriel and Schölkopf (JMLR 2018, Thm. 12) by showing that there exist both bounded continuous ∫ s.p.d. kernels that do not metrize weak convergence and bounded continuous non-∫ s.p.d. kernels that do metrize it

arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]

2022


no image
Generalized Few-Shot Video Classification With Video Retrieval and Feature Generation

Xian, Y., Korbar, B., Douze, M., Torresani, L., Schiele, B., Akata, Z.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):8949-8961, December 2022 (article)

DOI [BibTex]

2022

DOI [BibTex]


no image
A survey of algorithmic recourse: contrastive explanations and consequential recommendations

Karimi, A., Barthe, G., Schölkopf, B., Valera, I.

ACM Computing Surveys, 55(5), Association for Computing Machinery (ACM), December 2022 (article)

arXiv link (url) DOI [BibTex]

arXiv link (url) DOI [BibTex]


no image
Estimation of skeletal kinematics in freely moving rodents

Monsees, A., Voit, K., Wallace, D. J., Sawinski, J., Charyasz, E., Scheffler, K., Macke, J. H., Kerr, J. N. D.

Nature Methods, 19(11):1500-1509, November 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Quantifying the effects of contact tracing, testing, and containment measures in the presence of infection hotspots

Lorch, L., Kremer, H., Trouleau, W., Tsirtsis, S., Szanto, A., Schölkopf, B., Gomez-Rodriguez, M.

ACM Transactions on Spatial Algorithms and Systems, 8(4):article no. 25, November 2022 (article)

arXiv DOI Project Page [BibTex]

arXiv DOI Project Page [BibTex]


no image
Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies

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

Scientific Reports, 12(1):article no. 18733, November 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions

Gatidis, S., Hebb, T., Frueh, M., La Fougère, C., Nikolaou, K., Pfannenberg, C., Schölkopf, B., Kuestner, T., Cyran, C., Rubin, D.

Scientific Data, 9(1), October 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Sample-Efficient Policy Adaptation for Exoskeletons Under Variations in the Users and the Environment

Shahrokhshahi, A., Khadiv, M., Taherifar, A., Mansouri, S., Park, E. J., Arzanpour, S.

IEEE Robotics and Automation Letters, 7(4):9020-9027, October 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort

Frueh, M., Kuestner, T., Nachbar, M., Thorwarth, D., Schilling, A., Gatidis, S.

Computer Methods and Programs in Biomedicine, 225, pages: 107085, October 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
A Generative Model for Quasar Spectra

Eilers, A., Hogg, D. W., Schölkopf, B., Foreman-Mackey, D., Davies, F. B., Schindler, J.

The Astrophysical Journal, 938(1), The American Astronomical Society, October 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Causal Feature Selection via Orthogonal Search

Soleymani*, A., Raj*, A., Bauer, S., Schölkopf, B., Besserve, M.

Transactions on Machine Learning Research, September 2022, *equal contribution (article)

link (url) [BibTex]

link (url) [BibTex]


no image
Energy-efficient network activity from disparate circuit parameters

Deistler, M., Macke, J. H., Gonçalves, P. J.

Proceedings of the National Academy of Sciences, 119(44), September 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

Chen, Y., Mancini, M., Zhu, X., Akata, Z.

IEEE Transactions on Pattern Analysis and Machine Intelligence, August 2022, *early access (article)

DOI [BibTex]

DOI [BibTex]


no image
Real Time Landmark Detection for Within- and Cross Subject Tracking With Minimal Human Supervision

Frueh, M., Schilling, A., Gatidis, S., Kuestner, T.

IEEE Access, 10, pages: 81192-81202, August 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Optimal Client Sampling for Federated Learning

Chen, W., Horváth, S., Richtárik, P.

Transactions on Machine Learning Research, August 2022 (article)

link (url) [BibTex]

link (url) [BibTex]


no image
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

Oesterle, J., Krämer, N., P., H., Berens, P.

Journal of Computational Neuroscience, 50(4):485-503, August 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

Bing, S., Dittadi, A., Bauer, S., Schwab, P.

PLOS Digital Health, 1(7):e0000074, July 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Flexible and efficient simulation-based inference for models of decision-making

Boelts, J., Lueckmann, J., Gao, R., Macke, J. H.

eLife, 11, pages: e77220, eLife Sciences Publications, Ltd, July 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
T2 mapping for the characterization of prostate lesions

Hepp, T., Kalmbach, L., Kolb, M., Martirosian, P., Hilbert, T., Thaiss, W. M., Notohamiprodjo, M., Bedke, J., Nikolaou, K., Stenzl, A., Kruck, S., Kaufmann, S.

World Journal of Urology, 40(6):1455–1461, June 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy

Peisen, F., Hänsch, A., Hering, A., Brendlin, A. S., Afat, S., Nikolaou, K., Gatidis, S., Eigentler, T., Amaral, T., Moltz, J. H., Othman, A. E.

Cancers, 14(12):2992, June 2022 (article)

DOI [BibTex]

DOI [BibTex]


Complex interlinkages, key objectives and nexuses amongst the Sustainable Development Goals and climate change: a network analysis
Complex interlinkages, key objectives and nexuses amongst the Sustainable Development Goals and climate change: a network analysis

Laumann, F., von Kügelgen, J., Kanashiro Uehara, T. H., Barahona, M.

The Lancet Planetary Health, 6(5):e422-e430, May 2022 (article)

DOI [BibTex]

DOI [BibTex]


no image
The unpopular Package: A Data-driven Approach to Detrending TESS Full-frame Image Light Curves

Hattori, S., Foreman-Mackey, D., Hogg, D. W., Montet, B. T., Angus, R., Pritchard, T. A., Curtis, J. L., Schölkopf, B.

The Astronomical Journal, 163(6):article no. 284, May 2022 (article)

arXiv DOI [BibTex]

arXiv DOI [BibTex]