Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models.

Jonas Peters https://scholar.google.com/citations?user=kBQ4VvEAAAAJ https://mathgenealogy.org/id.php?id=171583 https://orcid.org/0000-0002-1487-7511 https://www.wikidata.org/entity/Q66305663 https://d-nb.info/gnd/1177834510 https://id.loc.gov/authorities/names/n2017055154 https://zbmath.org/authors/?q=ai:peters.jonas

Nicola Gnecco Jonas Peters Sebastian Engelke Niklas Pfister Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models. 46:1-46:57 2026 27 J. Mach. Learn. Res. https://jmlr.org/papers/v27/24-2207.html db/journals/jmlr/jmlr27.html#GneccoPEP26

Felix Schur Niklas Pfister Peng Ding Sach Mukherjee Jonas Peters Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible? 2026 January abs/2601.15254 CoRR https://doi.org/10.48550/arXiv.2601.15254 db/journals/corr/corr2601.html#abs-2601-15254 streams/journals/corr

Sorawit Saengkyongam Juan L. Gamella Andrew C. Miller Jonas Peters Nicolai Meinshausen Christina Heinze-Deml Anti-causal domain generalization: Leveraging unlabeled data. 2026 February abs/2602.17187 CoRR https://doi.org/10.48550/arXiv.2602.17187 db/journals/corr/corr2602.html#abs-2602-17187 streams/journals/corr

Margherita Lazzaretto Jonas Peters Niklas Pfister Invariant Subspace Decomposition. 95:1-95:56 2025 26 J. Mach. Learn. Res. https://jmlr.org/papers/v26/24-0699.html db/journals/jmlr/jmlr26.html#LazzarettoPP25

Juan L. Gamella Jonas Peters Peter Bühlmann Causal chambers as a real-world physical testbed for AI methodology. 107-118 2025 7 Nat. Mac. Intell. 1 https://doi.org/10.1038/s42256-024-00964-x db/journals/natmi/natmi7.html#GamellaPB25 streams/journals/natmi

Julius von Kügelgen Jakob Ketterer Xinwei Shen Nicolai Meinshausen Jonas Peters Representation Learning for Distributional Perturbation Extrapolation. 2025 April abs/2504.18522 CoRR https://doi.org/10.48550/arXiv.2504.18522 db/journals/corr/corr2504.html#abs-2504-18522 streams/journals/corr

Manuel Iglesias-Alonso Felix Schur Julius von Kügelgen Jonas Peters Transferring Causal Effects using Proxies. 2025 October abs/2510.25924 CoRR https://doi.org/10.48550/arXiv.2510.25924 db/journals/corr/corr2510.html#abs-2510-25924 streams/journals/corr

Francesco Freni Anya Fries Linus Kühne Markus Reichstein Jonas Peters Maximum Risk Minimization with Random Forests. 2025 December abs/2512.10445 CoRR https://doi.org/10.48550/arXiv.2512.10445 db/journals/corr/corr2512.html#abs-2512-10445 streams/journals/corr

Sorawit Saengkyongam Niklas Pfister Predrag Klasnja Susan A. Murphy Jonas Peters Effect-Invariant Mechanisms for Policy Generalization. 34:1-34:36 2024 25 J. Mach. Learn. Res. https://jmlr.org/papers/v25/23-0802.html db/journals/jmlr/jmlr25.html#SaengkyongamPKM24

Nikolaj Thams Rikke Søndergaard Sebastian Weichwald Jonas Peters Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past. 302:1-302:51 2024 25 J. Mach. Learn. Res. https://jmlr.org/papers/v25/22-0262.html db/journals/jmlr/jmlr25.html#ThamsSWP24

Sorawit Saengkyongam Elan Rosenfeld Pradeep Kumar Ravikumar Niklas Pfister Jonas Peters Identifying Representations for Intervention Extrapolation. 2024 ICLR https://openreview.net/forum?id=3cuJwmPxXj conf/iclr/2024 db/conf/iclr/iclr2024.html#SaengkyongamRRP24

Margherita Lazzaretto Jonas Peters Niklas Pfister Invariant Subspace Decomposition. 2024 abs/2404.09962 CoRR https://doi.org/10.48550/arXiv.2404.09962 db/journals/corr/corr2404.html#abs-2404-09962

Juan L. Gamella Jonas Peters Peter Bühlmann The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology. 2024 abs/2404.11341 CoRR https://doi.org/10.48550/arXiv.2404.11341 db/journals/corr/corr2404.html#abs-2404-11341

Felix Schur Jonas Peters DecoR: Deconfounding Time Series with Robust Regression. 2024 abs/2406.07005 CoRR https://doi.org/10.48550/arXiv.2406.07005 db/journals/corr/corr2406.html#abs-2406-07005 streams/journals/corr

Sorawit Saengkyongam Nikolaj Thams Jonas Peters Niklas Pfister Invariant Policy Learning: A Causal Perspective. 8606-8620 2023 July 45 IEEE Trans. Pattern Anal. Mach. Intell. 7 https://doi.org/10.1109/TPAMI.2022.3232363 db/journals/pami/pami45.html#SaengkyongamTPP23

Frederik Hytting Jørgensen Sebastian Weichwald Jonas Peters Unfair Utilities and First Steps Towards Improving Them. 2023 abs/2306.00636 CoRR https://doi.org/10.48550/arXiv.2306.00636 db/journals/corr/corr2306.html#abs-2306-00636

Sorawit Saengkyongam Niklas Pfister Predrag V. Klasnja Susan A. Murphy Jonas Peters Effect-Invariant Mechanisms for Policy Generalization. 2023 abs/2306.10983 CoRR https://doi.org/10.48550/arXiv.2306.10983 db/journals/corr/corr2306.html#abs-2306-10983

Sorawit Saengkyongam Elan Rosenfeld Pradeep Ravikumar Niklas Pfister Jonas Peters Identifying Representations for Intervention Extrapolation. 2023 abs/2310.04295 CoRR https://doi.org/10.48550/arXiv.2310.04295 db/journals/corr/corr2310.html#abs-2310-04295

Nicola Gnecco Jonas Peters Sebastian Engelke Niklas Pfister Boosted Control Functions. 2023 abs/2310.05805 CoRR https://doi.org/10.48550/arXiv.2310.05805 db/journals/corr/corr2310.html#abs-2310-05805

Martin Emil Jakobsen Rajen Dinesh Shah Peter Bühlmann Jonas Peters Structure Learning for Directed Trees. 159:1-159:97 2022 23 J. Mach. Learn. Res. https://jmlr.org/papers/v23/21-1159.html db/journals/jmlr/jmlr23.html#JakobsenSBP22

Rune Christiansen Niklas Pfister Martin Emil Jakobsen Nicola Gnecco Jonas Peters A Causal Framework for Distribution Generalization. 6614-6630 2022 44 IEEE Trans. Pattern Anal. Mach. Intell. 10 https://doi.org/10.1109/TPAMI.2021.3094760 db/journals/pami/pami44.html#ChristiansenPJG22

Phillip B. Mogensen Nikolaj Thams Jonas Peters Invariant Ancestry Search. 15832-15857 2022 ICML https://proceedings.mlr.press/v162/mogensen22a.html conf/icml/2022 db/conf/icml/icml2022.html#MogensenTP22 Sorawit Saengkyongam Leonard Henckel Niklas Pfister Jonas Peters Exploiting Independent Instruments: Identification and Distribution Generalization. 18935-18958 2022 ICML https://proceedings.mlr.press/v162/saengkyongam22a.html conf/icml/2022 db/conf/icml/icml2022.html#SaengkyongamHPP22 Niklas Pfister Jonas Peters Identifiability of sparse causal effects using instrumental variables. 1613-1622 2022 UAI https://proceedings.mlr.press/v180/pfister22a.html conf/uai/2022 db/conf/uai/uai2022.html#PfisterP22 Jonas Peters Stefan Bauer Niklas Pfister Causal Models for Dynamical Systems. 671-690 2022 Probabilistic and Causal Inference https://doi.org/10.1145/3501714.3501752 books/acm/22/GDH2022 db/books/collections/GDH2022.html#PetersBP22

Phillip B. Mogensen Nikolaj Thams Jonas Peters Invariant Ancestry Search. 2022 abs/2202.00913 CoRR https://arxiv.org/abs/2202.00913 db/journals/corr/corr2202.html#abs-2202-00913

Sorawit Saengkyongam Leonard Henckel Niklas Pfister Jonas Peters Exploiting Independent Instruments: Identification and Distribution Generalization. 2022 abs/2202.01864 CoRR https://arxiv.org/abs/2202.01864 db/journals/corr/corr2202.html#abs-2202-01864

Sebastian Weichwald Søren Wengel Mogensen Tabitha Edith Lee Dominik Baumann Oliver Kroemer Isabelle Guyon Sebastian Trimpe Jonas Peters Niklas Pfister Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. 2022 abs/2202.06052 CoRR https://arxiv.org/abs/2202.06052 db/journals/corr/corr2202.html#abs-2202-06052

Sebastian Weichwald Jonas Peters Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness. 226-247 2021 33 J. Cogn. Neurosci. 2 https://doi.org/10.1162/jocn_a_01623 https://www.wikidata.org/entity/Q98566094 db/journals/jocn/jocn33.html#WeichwaldP21

Michael Oberst Nikolaj Thams Jonas Peters David A. Sontag Regularizing towards Causal Invariance: Linear Models with Proxies. 8260-8270 2021 ICML http://proceedings.mlr.press/v139/oberst21a.html conf/icml/2021 db/conf/icml/icml2021.html#OberstTPS21 Sebastian Weichwald Søren Wengel Mogensen Tabitha Edith Lee Dominik Baumann Oliver Kroemer Isabelle Guyon Sebastian Trimpe Jonas Peters Niklas Pfister Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. 246-258 2021 NeurIPS (Competition and Demos) https://proceedings.mlr.press/v176/weichwald22a.html conf/nips/2021demo db/conf/nips/demo2021x.html#WeichwaldMLBKGT21

Michael Oberst Nikolaj Thams Jonas Peters David A. Sontag Regularizing towards Causal Invariance: Linear Models with Proxies. 2021 abs/2103.02477 CoRR https://arxiv.org/abs/2103.02477 db/journals/corr/corr2103.html#abs-2103-02477

Sorawit Saengkyongam Nikolaj Thams Jonas Peters Niklas Pfister Invariant Policy Learning: A Causal Perspective. 2021 abs/2106.00808 CoRR https://arxiv.org/abs/2106.00808 db/journals/corr/corr2106.html#abs-2106-00808

Martin Emil Jakobsen Rajen Dinesh Shah Peter Bühlmann Jonas Peters Structure Learning for Directed Trees. 2021 abs/2108.08871 CoRR https://arxiv.org/abs/2108.08871 db/journals/corr/corr2108.html#abs-2108-08871

Rune Christiansen Jonas Peters Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables. 41:1-41:46 2020 21 J. Mach. Learn. Res. https://jmlr.org/papers/v21/19-407.html db/journals/jmlr/jmlr21.html#ChristiansenP20

Martin Emil Jakobsen Jonas Peters Distributional Robustness of K-class Estimators and the PULSE. 2020 abs/2005.03353 CoRR https://arxiv.org/abs/2005.03353 db/journals/corr/corr2005.html#abs-2005-03353

Mateo Rojas-Carulla Bernhard Schölkopf Richard E. Turner Jonas Peters Invariant Models for Causal Transfer Learning. 2018 19 J. Mach. Learn. Res. https://jmlr.org/papers/v19/16-432.html db/journals/jmlr/jmlr19.html#Rojas-CarullaST18 36:1-36:34

Niklas Pfister Stefan Bauer Jonas Peters Identifying Causal Structure in Large-Scale Kinetic Systems. 2018 abs/1810.11776 CoRR http://arxiv.org/abs/1810.11776 db/journals/corr/corr1810.html#abs-1810-11776

Joris M. Mooij Jonas Peters Dominik Janzing Jakob Zscheischler Bernhard Schölkopf Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. 32:1-32:102 2016 17 J. Mach. Learn. Res. https://jmlr.org/papers/v17/14-518.html db/journals/jmlr/jmlr17.html#MooijPJZS16

Bernhard Schölkopf David W. Hogg Dun Wang Daniel Foreman-Mackey Dominik Janzing Carl-Johann Simon-Gabriel Jonas Peters Modeling confounding by half-sibling regression. 7391-7398 2016 113 Proc. Natl. Acad. Sci. USA 27 https://doi.org/10.1073/pnas.1511656113 https://www.wikidata.org/entity/Q37086395 db/journals/pnas/pnas113.html#ScholkopfHWFJSP16

Stefan Bauer Bernhard Schölkopf Jonas Peters The Arrow of Time in Multivariate Time Series. 2043-2051 2016 ICML http://proceedings.mlr.press/v48/bauer16.html conf/icml/2016 db/conf/icml/icml2016.html#BauerSP16

Stephan Bongers Jonas Peters Bernhard Schölkopf Joris M. Mooij Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. 2016 abs/1611.06221 CoRR http://arxiv.org/abs/1611.06221 db/journals/corr/corr1611.html#BongersPSM16

Jonas Peters Peter Bühlmann Structural Intervention Distance for Evaluating Causal Graphs. 771-799 2015 27 Neural Comput. 3 https://doi.org/10.1162/NECO_a_00708 https://www.wikidata.org/entity/Q50974236 db/journals/neco/neco27.html#PetersB15

Bernhard Schölkopf Krikamol Muandet Kenji Fukumizu Stefan Harmeling Jonas Peters Computing functions of random variables via reproducing kernel Hilbert space representations. 755-766 2015 25 Stat. Comput. 4 https://doi.org/10.1007/s11222-015-9558-5 https://www.wikidata.org/entity/Q59401997 db/journals/sac/sac25.html#ScholkopfMFHP15

Bernhard Schölkopf David W. Hogg Dun Wang Daniel Foreman-Mackey Dominik Janzing Carl-Johann Simon-Gabriel Jonas Peters Removing systematic errors for exoplanet search via latent causes. 2218-2226 2015 ICML http://proceedings.mlr.press/v37/scholkopf15.html conf/icml/2015 db/conf/icml/icml2015.html#ScholkopfHWFJSP15 Dominik Rothenhäusler Christina Heinze Jonas Peters Nicolai Meinshausen BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. 1513-1521 2015 NIPS https://proceedings.neurips.cc/paper/2015/hash/92262bf907af914b95a0fc33c3f33bf6-Abstract.html http://papers.nips.cc/paper/5682-backshift-learning-causal-cyclic-graphs-from-unknown-shift-interventions conf/nips/2015 db/conf/nips/nips2015.html#RothenhauslerHP15 Ricardo Silva 0001 Ilya Shpitser Robin J. Evans 0002 Jonas Peters Tom Claassen Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015. ACI@UAI CEUR-WS.org CEUR Workshop Proceedings 1504 2015 https://ceur-ws.org/Vol-1504 https://nbn-resolving.org/urn:nbn:de:0074-1504-0 db/conf/uai/aci2015.html

Bernhard Schölkopf Krikamol Muandet Kenji Fukumizu Jonas Peters Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. 2015 abs/1501.06794 CoRR http://arxiv.org/abs/1501.06794 db/journals/corr/corr1501.html#ScholkopfMFP15

Jonas Peters Joris M. Mooij Dominik Janzing Bernhard Schölkopf Causal discovery with continuous additive noise models. 2009-2053 2014 15 J. Mach. Learn. Res. 1 https://dl.acm.org/doi/10.5555/2627435.2670315 db/journals/jmlr/jmlr15.html#PetersMJS14

Joris M. Mooij Dominik Janzing Jonas Peters Tom Claassen Antti Hyttinen Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014. CI@UAI CEUR-WS.org CEUR Workshop Proceedings 1274 2014 https://ceur-ws.org/Vol-1274 https://nbn-resolving.org/urn:nbn:de:0074-1274-4 db/conf/uai/ci2014.html

Joris M. Mooij Jonas Peters Dominik Janzing Jakob Zscheischler Bernhard Schölkopf Distinguishing cause from effect using observational data: methods and benchmarks. 2014 abs/1412.3773 CoRR http://arxiv.org/abs/1412.3773 db/journals/corr/corr1412.html#MooijPJZS14

Léon Bottou Jonas Peters Joaquin Quiñonero Candela Denis Xavier Charles Max Chickering Elon Portugaly Dipankar Ray Patrice Y. Simard Ed Snelson Counterfactual reasoning and learning systems: the example of computational advertising. 3207-3260 2013 14 J. Mach. Learn. Res. 1 https://dl.acm.org/doi/10.5555/2567709.2567766 db/journals/jmlr/jmlr14.html#BottouPCCCPRSS13

Bernhard Schölkopf Dominik Janzing Jonas Peters Eleni Sgouritsa Kun Zhang 0001 Joris M. Mooij Semi-supervised Learning in Causal and Anticausal Settings. 129-141 2013 Empirical Inference https://doi.org/10.1007/978-3-642-41136-6_13 https://www.wikidata.org/entity/Q57340936 conf/birthday/2013vapnik db/conf/birthday/vapnik2013.html#ScholkopfJPS0M13 Jonas Peters Dominik Janzing Bernhard Schölkopf Causal Inference on Time Series using Restricted Structural Equation Models. 154-162 2013 NIPS https://proceedings.neurips.cc/paper/2013/hash/47d1e990583c9c67424d369f3414728e-Abstract.html http://papers.nips.cc/paper/5063-causal-inference-on-time-series-using-restricted-structural-equation-models conf/nips/2013 db/conf/nips/nips2013.html#PetersJS13 Eleni Sgouritsa Dominik Janzing Jonas Peters Bernhard Schölkopf Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. 2013 UAI https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=2417&proceeding_id=29 conf/uai/2013 db/conf/uai/uai2013.html#SgouritsaJPS13

Eleni Sgouritsa Dominik Janzing Jonas Peters Bernhard Schölkopf Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. 2013 CoRR http://arxiv.org/abs/1309.6860 abs/1309.6860 db/journals/corr/corr1309.html#SgouritsaJPS13

Peter Bühlmann Jonas Peters Jan Ernest CAM: Causal Additive Models, high-dimensional order search and penalized regression. 2013 CoRR http://arxiv.org/abs/1310.1533 abs/1310.1533 db/journals/corr/corr1310.html#BuhlmannPE13

Bernhard Schölkopf Dominik Janzing Jonas Peters Eleni Sgouritsa Kun Zhang 0001 Joris M. Mooij On causal and anticausal learning. 2012 ICML http://icml.cc/2012/papers/625.pdf conf/icml/2012 db/conf/icml/icml2012.html#ScholkopfJPSZM12

Dominik Janzing Eleni Sgouritsa Oliver Stegle Jonas Peters Bernhard Schölkopf Detecting low-complexity unobserved causes http://arxiv.org/abs/1202.3737 2012 CoRR abs/1202.3737 db/journals/corr/corr1202.html#abs-1202-3737

Jonas Peters Joris M. Mooij Dominik Janzing Bernhard Schölkopf Identifiability of Causal Graphs using Functional Models http://arxiv.org/abs/1202.3757 2012 CoRR abs/1202.3757 db/journals/corr/corr1202.html#abs-1202-3757

Kun Zhang 0001 Jonas Peters Dominik Janzing Bernhard Schölkopf Kernel-based Conditional Independence Test and Application in Causal Discovery http://arxiv.org/abs/1202.3775 2012 CoRR abs/1202.3775 db/journals/corr/corr1202.html#abs-1202-3775

Dominik Janzing Jonas Peters Joris M. Mooij Bernhard Schölkopf Identifying confounders using additive noise models http://arxiv.org/abs/1205.2640 2012 CoRR abs/1205.2640 db/journals/corr/corr1205.html#abs-1205-2640

Jonas Peters Dominik Janzing Bernhard Schölkopf Causal Inference on Time Series using Structural Equation Models http://arxiv.org/abs/1207.5136 2012 CoRR abs/1207.5136 db/journals/corr/corr1207.html#abs-1207-5136

Léon Bottou Jonas Peters Joaquin Quiñonero Candela Denis Xavier Charles Max Chickering Elon Portugaly Dipankar Ray Patrice Y. Simard Ed Snelson Counterfactual Reasoning and Learning Systems http://arxiv.org/abs/1209.2355 2012 CoRR abs/1209.2355 db/journals/corr/corr1209.html#abs-1209-2355

Jonas Peters Dominik Janzing Bernhard Schölkopf Causal Inference on Discrete Data Using Additive Noise Models. 2436-2450 2011 33 IEEE Trans. Pattern Anal. Mach. Intell. 12 https://doi.org/10.1109/TPAMI.2011.71 http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.71 https://www.wikidata.org/entity/Q33862287 db/journals/pami/pami33.html#PetersJS11

Dominik Janzing Eleni Sgouritsa Oliver Stegle Jonas Peters Bernhard Schölkopf Detecting low-complexity unobserved causes. 383-391 2011 UAI https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=2185&proceeding_id=27 conf/uai/2011 db/conf/uai/uai2011.html#JanzingSSPS11 Jonas Peters Joris M. Mooij Dominik Janzing Bernhard Schölkopf Identifiability of Causal Graphs using Functional Models. 589-598 2011 UAI https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=2216&proceeding_id=27 conf/uai/2011 db/conf/uai/uai2011.html#PetersMJS11 Kun Zhang 0001 Jonas Peters Dominik Janzing Bernhard Schölkopf Kernel-based Conditional Independence Test and Application in Causal Discovery. 804-813 2011 UAI https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=2233&proceeding_id=27 conf/uai/2011 db/conf/uai/uai2011.html#ZhangPJS11

Bernhard Schölkopf Dominik Janzing Jonas Peters Kun Zhang 0001 Robust Learning via Cause-Effect Models http://arxiv.org/abs/1112.2738 2011 CoRR abs/1112.2738 db/journals/corr/corr1112.html#abs-1112-2738

Jonas Peters Dominik Janzing Bernhard Schölkopf Identifying Cause and Effect on Discrete Data using Additive Noise Models. 597-604 AISTATS 2010 conf/aistats/2010 http://proceedings.mlr.press/v9/peters10a.html db/journals/jmlr/jmlrp9.html#PetersJS10 Joris M. Mooij Dominik Janzing Jonas Peters Bernhard Schölkopf Regression by dependence minimization and its application to causal inference in additive noise models. 745-752 2009 ICML https://doi.org/10.1145/1553374.1553470 conf/icml/2009 db/conf/icml/icml2009.html#MooijJPS09 Jonas Peters Dominik Janzing Arthur Gretton Bernhard Schölkopf Detecting the direction of causal time series. 801-808 2009 ICML https://doi.org/10.1145/1553374.1553477 conf/icml/2009 db/conf/icml/icml2009.html#PetersJGS09 Dominik Janzing Jonas Peters Joris M. Mooij Bernhard Schölkopf Identifying confounders using additive noise models. 249-257 2009 UAI https://www.auai.org/uai2009/papers/UAI2009_0118_d5787036a879b5fb06ab513f53eb06a7.pdf conf/uai/2009 db/conf/uai/uai2009.html#JanzingPMS09 Jonas Peters Dominik Janzing Arthur Gretton Bernhard Schölkopf Kernel Methods for Detecting the Direction of Time Series. 57-66 2008 GfKl https://doi.org/10.1007/978-3-642-01044-6_5 https://www.wikidata.org/entity/Q57340961 conf/gfkl/2008 db/conf/gfkl/gfkl2008.html#PetersJGS08 Patrik O. Hoyer Dominik Janzing Joris M. Mooij Jonas Peters Bernhard Schölkopf Nonlinear causal discovery with additive noise models. 689-696 2008 NIPS https://proceedings.neurips.cc/paper/2008/hash/f7664060cc52bc6f3d620bcedc94a4b6-Abstract.html http://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models conf/nips/2008 db/conf/nips/nips2008.html#HoyerJMPS08 Stefan Bauer Dominik Baumann Stephan Bongers Léon Bottou Peter Bühlmann Joaquin Quiñonero Candela Denis Xavier Charles David Maxwell ChickeringMax Chickering Rune Christiansen Tom Claassen Peng Ding Sebastian Engelke Jan Ernest Robin J. Evans 0002 Daniel Foreman-Mackey Francesco Freni Anya Fries Kenji Fukumizu Juan L. Gamella Nicola Gnecco Arthur Gretton Isabelle Guyon Stefan Harmeling Christina Heinze-DemlChristina Heinze Leonard Henckel David W. Hogg Patrik O. Hoyer Antti Hyttinen Manuel Iglesias-Alonso Martin Emil Jakobsen Dominik Janzing Frederik Hytting Jørgensen Jakob Ketterer Predrag V. KlasnjaPredrag Klasnja Oliver Kroemer Julius von Kügelgen Linus Kühne Margherita Lazzaretto Tabitha Edith Lee Nicolai Meinshausen Andrew C. Miller Phillip B. Mogensen Søren Wengel Mogensen Joris M. Mooij Krikamol Muandet Sach Mukherjee Susan A. Murphy Michael Oberst Niklas Pfister Elon Portugaly Pradeep RavikumarPradeep Kumar Ravikumar Dipankar Ray Markus Reichstein Mateo Rojas-Carulla Elan Rosenfeld Dominik Rothenhäusler Sorawit Saengkyongam Bernhard Schölkopf Felix Schur Eleni Sgouritsa Rajen Dinesh Shah Xinwei Shen Ilya Shpitser Ricardo Silva 0001 Patrice Y. Simard Carl-Johann Simon-Gabriel Ed Snelson Rikke Søndergaard David A. Sontag Oliver Stegle Nikolaj Thams Sebastian Trimpe Richard E. Turner Dun Wang Sebastian Weichwald Kun Zhang 0001 Jakob Zscheischler