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Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

Feb 16, 2021
Junhyung Park, Uri Shalit, Bernhard Sch├Âlkopf, Krikamol Muandet


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An Optimal Witness Function for Two-Sample Testing

Feb 10, 2021
Jonas M. K├╝bler, Wittawat Jitkrittum, Bernhard Sch├Âlkopf, Krikamol Muandet

* Under Review - Code available upon personal request 

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Regularised Least-Squares Regression with Infinite-Dimensional Output Space

Oct 21, 2020
Junhyunng Park, Krikamol Muandet


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Maximum Moment Restriction for Instrumental Variable Regression

Oct 15, 2020
Rui Zhang, Masaaki Imaizumi, Bernhard Sch├Âlkopf, Krikamol Muandet

* 30 pages 

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Grasping Field: Learning Implicit Representations for Human Grasps

Aug 12, 2020
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, Siyu Tang


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Learning Kernel Tests Without Data Splitting

Jun 05, 2020
Jonas M. K├╝bler, Wittawat Jitkrittum, Bernhard Sch├Âlkopf, Krikamol Muandet

* 24 (10+14) pages, 9 figures. Under Review v2: added missing references and acknowledgments 

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A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

Mar 12, 2020
Junhyung Park, Krikamol Muandet


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Kernel Conditional Moment Test via Maximum Moment Restriction

Mar 07, 2020
Krikamol Muandet, Wittawat Jitkrittum, Jonas K├╝bler


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A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming

Nov 25, 2019
Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Sch├Âlkopf


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Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

Nov 03, 2019
Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Sch├Âlkopf

* There was a misunderstanding in how an article should be updated on arXiv. We have withdrawn this article from this link. The same article can be found at arXiv:1901.09206 

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Dual IV: A Single Stage Instrumental Variable Regression

Oct 27, 2019
Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj


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Low-rank Random Tensor for Bilinear Pooling

Jun 03, 2019
Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang

* 13 pages (3 pages appendix), 2 figures 

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Quantum Mean Embedding of Probability Distributions

May 31, 2019
Jonas M. K├╝bler, Krikamol Muandet, Bernhard Sch├Âlkopf

* 7 pages, 2 figures 

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Private Causal Inference using Propensity Scores

May 29, 2019
Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet


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Kernel Conditional Density Operators

May 27, 2019
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet


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Improving Consequential Decision Making under Imperfect Predictions

Feb 08, 2019
Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Sch├Âlkopf, Krikamol Muandet, Isabel Valera


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Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Jan 26, 2019
Arash Mehrjou, Wittawat Jitkrittum, Bernhard Sch├Âlkopf, Krikamol Muandet


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Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Jan 10, 2019
Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann

* 11 pages, 2 figures. A revised version towards a complete review 

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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

May 22, 2018
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat


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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

May 16, 2018
Stefan Klus, Ingmar Schuster, Krikamol Muandet


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Kernel Mean Embedding of Distributions: A Review and Beyond

Jan 25, 2017
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Bernhard Sch├Âlkopf

* Foundations and Trends in Machine Learning: Vol. 10: No. 1-2, pp 1-141 (2017) 
* 147 pages; this is a version of the manuscript after the review process 

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Kernel Mean Shrinkage Estimators

Feb 25, 2016
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Sch├Âlkopf

* 41 pages 

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Towards a Learning Theory of Cause-Effect Inference

May 18, 2015
David Lopez-Paz, Krikamol Muandet, Bernhard Sch├Âlkopf, Ilya Tolstikhin


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