2023
Bottou, L., Schölkopf, B.
Borges und die Künstliche Intelligenz
2023, published in Frankfurter Allgemeine Zeitung, 18 December 2023, Nr. 294 (misc)
2022
Wang, H., Jin, Z., Cao, J., Fung, G. P. C., Wong, K.
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
2022 (misc)
2021
Prabhoo, S., Bauer, S., Schwab, P.
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
2021 (misc)
Abdulsamad, H., Dorau, T., Belousov, B., Zhu, J., Peters, J.
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions
2021 (misc)
Arvanitidis, G., González Duque, M., Pouplin, A., Kalatzis, D., Hauberg, S.
Pulling back information geometry
2021 (misc)
Wüthrich*, M., Widmaier*, F., Bauer*, S., Funk, N., Urain, J., Peters, J., Watson, J., Chen, C., Srinivasan, K., Zhang, J., Zhang, J., Walter, M. R., Madan, R., Schaff, C., Maeda, T., Yoneda, T., Yarats, D., Allshire, A., Gordon, E. K., Bhattacharjee, T., Srinivasa, S. S., Garg, A., Buchholz, A., Stark, S., Steinbrenner, T., Akpo, J., Joshi, S., Agrawal, V., Schölkopf, B.
A Robot Cluster for Reproducible Research in Dexterous Manipulation
2021, *equal contribution (misc)
Belousov, B., H., A., Klink, P., Parisi, S., Peters, J.
Reinforcement Learning Algorithms: Analysis and Applications
883, Studies in Computational Intelligence, Springer International Publishing, 2021 (book)
Shao, K., Villegas, J. F. R., Logothetis, N. K., Besserve, M.
A model of Ponto-Geniculo-Occipital waves supports bidirectional control of cortical plasticity across sleep-stages
2021 (misc) In preparation
Georgiev, B., Franken, L., Mukherjee, M., Arvanitidis, G.
On the Impact of Stable Ranks in Deep Nets
2021 (misc)
Allshire, A., Mittal, M., Lodaya, V., Makoviychuk, V., Makoviichuk, D., Widmaier, F., Wüthrich, M., Bauer, S., Handa, A., Garg, A.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
2021 (misc)
2020
Chicharro, D., Besserve, M., Panzeri, S.
Causal learning with sufficient statistics: an information bottleneck approach
2020 (misc) Submitted
Tosatto, S., Stadtmueller, J., Peters, J.
Dimensionality Reduction of Movement Primitives in Parameter Space
2020 (misc)
Ke, R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y.
Learning Neural Causal Models from Unknown Interventions
2020 (misc)
2019
Lutz, P.
Automatic Segmentation and Labelling for Robot Table Tennis Time Series
Technical University Darmstadt, Germany, August 2019 (thesis)
Park, M., Jitkrittum, W.
ABCDP: Approximate Bayesian Computation Meets Differential Privacy
2019 (misc) Submitted
Scientific Report 2016 - 2018
2019 (mpi_year_book)
Pfister, N., Bauer, S., Peters, J.
Identifying Causal Structure in Large-Scale Kinetic Systems
2019 (misc)
Tanneberg, D., Rueckert, E., Peters, J.
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture
2019 (misc)
2018
Schölkopf, B.
Die kybernetische Revolution
S{\"u}ddeutsche Zeitung, 2018, (15-Mar-2018) (misc)
Veiga, F. F., Edin, B. B., Peters, J.
In-Hand Object Stabilization by Independent Finger Control
2018 (misc)
Garreau, D., Jitkrittum, W., Kanagawa, M.
Large sample analysis of the median heuristic
2018 (misc) In preparation
2017
Peters, J., Janzing, D., Schölkopf, B.
Elements of Causal Inference - Foundations and Learning Algorithms
Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)
Bousquet, O., Gelly, S., Tolstikhin, I., Simon-Gabriel, C. J., Schölkopf, B.
From Optimal Transport to Generative Modeling: the VEGAN cookbook
2017 (misc)
Belousov, B., Peters, J.
f-Divergence constrained policy improvement
2017 (misc)
2016
Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)
Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T.
Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set
2016 (misc)
2014
Kober, J., Peters, J.
Learning Motor Skills: From Algorithms to Robot Experiments
97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)
2013
Schölkopf, B., Luo, Z., Vovk, V.
Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik
Springer, 2013 (book)
2011
Sra, S., Nowozin, S., Wright, S.
Optimization for Machine Learning
pages: 494, Neural information processing series, MIT Press, Cambridge, MA, USA, December 2011 (book)
Barber, D., Cemgil, A., Chiappa, S.
Bayesian Time Series Models
pages: 432, Cambridge University Press, Cambridge, UK, August 2011 (book)
Lu, H., Schölkopf, B., Zhao, H.
Handbook of Statistical Bioinformatics
pages: 627, Springer Handbooks of Computational Statistics, Springer, Berlin, Germany, 2011 (book)
2010
Sigaud, O., Peters, J.
From Motor Learning to Interaction Learning in Robots
pages: 538, Studies in Computational Intelligence ; 264, (Editors: O Sigaud, J Peters), Springer, Berlin, Germany, January 2010 (book)
2007
Bakir, G., Hofmann, T., Schölkopf, B., Smola, A., Taskar, B., Vishwanathan, S.
Predicting Structured Data
pages: 360, Advances in neural information processing systems, MIT Press, Cambridge, MA, USA, September 2007 (book)
Wichman, F., Ernst, MO.
Mathematik der Wahrnehmung: Wendepunkte
Akademische Mitteilungen zw{\"o}lf: F{\"u}nf Sinne, pages: 32-37, 2007 (misc)
2006
Chapelle, O., Schölkopf, B., Zien, A.
Semi-Supervised Learning
pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)
Rasmussen, CE., Williams, CKI.
Gaussian Processes for Machine Learning
pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)
2004
Schölkopf, B., Tsuda, K., Vert, J.
Kernel Methods in Computational Biology
pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)
Schölkopf, B.
Statistische Lerntheorie und Empirische Inferenz
Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)
2002
Schölkopf, B., Smola, A.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
pages: 644, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, December 2002, Parts of this book, including an introduction to kernel methods, can be downloaded here. (book)
2000
Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D.
Advances in Large Margin Classifiers
pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)
Scientific Report 2016 - 2021
(mpi_year_book)