2023
Jenny, D.
Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures
ETH Zurich, Switzerland, November 2023, external supervision (thesis)
Jin, Z., Mihalcea, R.
Natural Language Processing for Policymaking
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)
2022
Biester, L., Demszky, D., Jin, Z., Sachan, M., Tetreault, J., Wilson, S., Xiao, L., Zhao, J.
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
Association for Computational Linguistics, December 2022 (proceedings)
Schölkopf, B.
Causality, causal digital twins, and their applications
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382), (Editors: Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica), September 2022 (talk)
Schölkopf, B., Uhler, C., Zhang, K.
Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022)
177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)
Peters, J., Bauer, S., Pfister, N.
Causal Models for Dynamical Systems
In Probabilistic and Causal Inference: The Works of Judea Pearl, pages: 671-690, 1, Association for Computing Machinery, 2022 (inbook)
Karimi, A. H., von Kügelgen, J., Schölkopf, B., Valera, I.
Towards Causal Algorithmic Recourse
In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pages: 139-166, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (inbook)
Schölkopf, B.
Causality for Machine Learning
In Probabilistic and Causal Inference: The Works of Judea Pearl, pages: 765-804, 1, Association for Computing Machinery, New York, NY, USA, 2022 (inbook)
Salewski, L., Koepke, A. S., Lensch, H. P. A., Akata, Z.
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pages: 69-88, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (inbook)
2021
Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C.
Proceedings of the 1st Workshop on NLP for Positive Impact
Association for Computational Linguistics, August 2021 (proceedings)
Scientific Report 2016 - 2021
2021 (mpi_year_book)
2019
Safavi, S., Logothetis, N., Besserve, M.
Multivariate coupling estimation between continuous signals and point processes
Neural Information Processing Systems 2019 - Workshop on Learning with Temporal Point Processes, December 2019 (talk)
Lutz, P.
Automatic Segmentation and Labelling for Robot Table Tennis Time Series
Technical University Darmstadt, Germany, August 2019 (thesis)
Scientific Report 2016 - 2018
2019 (mpi_year_book)
2018
Schölkopf, B.
Maschinelles Lernen: Entwicklung ohne Grenzen?
In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)
Wichmann, F. A., Jäkel, F.
Methods in Psychophysics
In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)
Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.
Transfer Learning for BCIs
In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)
2017
Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S.
Robot Learning
In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)
Peters, J., Bagnell, J.
Policy Gradient Methods
In Encyclopedia of Machine Learning and Data Mining, pages: 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)
Flad, N., Fomina, T., Bülthoff, H. H., Chuang, L. L.
Unsupervised clustering of EOG as a viable substitute for optical eye-tracking
In First Workshop on Eye Tracking and Visualization (ETVIS 2015), pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)
Peters, J., Tedrake, R., Roy, N., Morimoto, J.
Robot Learning
In Encyclopedia of Machine Learning and Data Mining, pages: 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)
Janzing, D.
Statistical Asymmetries Between Cause and Effect
In Time in Physics, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)
2016
Ihler, A. T., Janzing, D.
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI)
pages: 869 pages, AUAI Press, June 2016 (proceedings)
Zhang, K., Hyvärinen, A.
Nonlinear functional causal models for distinguishing cause from effect
In Statistics and Causality: Methods for Applied Empirical Research, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)
Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.
A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis
In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)
2015
Besserve, M.
Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism
53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)
Charpiat, G., Hofmann, M., Schölkopf, B.
Kernel methods in medical imaging
In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)
Besserve, M.
Independence of cause and mechanism in brain networks
DALI workshop on Networks: Processes and Causality, April 2015 (talk)
Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.
Information-Theoretic Implications of Classical and Quantum Causal Structures
18th Conference on Quantum Information Processing (QIP), 2015 (talk)
Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.
The search for single exoplanet transits in the Kepler light curves
IAU General Assembly, 22, pages: 2258352, 2015 (talk)
Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.
Justifying Information-Geometric Causal Inference
In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)
2014
Besserve, M., Schölkopf, B., Logothetis, N. K.
Unsupervised identification of neural events in local field potentials
44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)
Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.
Single-Source Domain Adaptation with Target and Conditional Shift
In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)
Besserve, M.
Quantifying statistical dependency
Research Network on Learning Systems Summer School, 2014 (talk)
Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.
Higher-Order Tensors in Diffusion Imaging
In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, pages: 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (inbook)
Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G.
Fuzzy Fibers: Uncertainty in dMRI Tractography
In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, pages: 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (inbook)
Sra, S.
Nonconvex Proximal Splitting with Computational Errors
In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (inbook)
Janzing, D.
Causal Inference from Passive Observations
24th Summer School University of Jyväskylā, Finland, August, 2014 (talk)
2013
Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.
Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI
Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience},
year = {2013},
month = {7},
volume = {14},
number = {Supplement 1},
pages = {A1}, (talk)
Grosse-Wentrup, M., Schölkopf, B.
A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)
In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)
Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.
Semi-supervised learning in causal and anticausal settings
In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)
Sra, S.
Tractable large-scale optimization in machine learning
In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)
Mantlik, F., Bezrukov, I., Hofmann, M., Schölkopf, B., Pichler, B.
MR-Based Attenuation Correction for Combined Brain PET/MR: Robustness of Atlas- and Pattern Recognition Method to Atlas Registration Failures
IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE MIC), 2013 (talk)
Deisenroth, M., Szepesvári, C., Peters, J.
Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24
pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)
Muandet, K.
Domain Generalization via Invariant Feature Representation
30th International Conference on Machine Learning (ICML2013), 2013 (talk)
Seldin, Y., Schölkopf, B.
On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension
In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)
2012
Muandet, K.
Support Vector Machines, Support Measure Machines, and Quasar Target Selection
Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)
Muandet, K.
Hilbert Space Embedding for Dirichlet Process Mixtures
NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)