Krikamol Muandet

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 64

Top Venues

University of Tübingen, Germany (PHD)

Education History

University of Tübingen, Germany

PHD

Publications

Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
Causal Strategic Learning with Competitive Selection. AAAI (2024)
Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau
Looping in the Human: Collaborative and Explainable Bayesian Optimization. AISTATS (2024)
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
Learning Counterfactually Invariant Predictors. Trans. Mach. Learn. Res. 2024 (2024)
Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
Domain Generalisation via Imprecise Learning. CoRR (2024)
Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar
Fast Adaptive Test-Time Defense with Robust Features. CoRR (2023)
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS (2023)
Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet
Gated Domain Units for Multi-source Domain Generalization. Trans. Mach. Learn. Res. 2023 (2023)
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR (2023)
Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
A Measure-Theoretic Axiomatisation of Causality. NeurIPS (2023)
Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
Causal Strategic Learning with Competitive Selection. CoRR (2023)
Junhyung Park, Krikamol Muandet
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes. ALT (2023)
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
On the Relationship Between Explanation and Prediction: A Causal View. ICML (2023)
Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
A Measure-Theoretic Axiomatisation of Causality. CoRR (2023)
Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau
Looping in the Human: Collaborative and Explainable Bayesian Optimization. CoRR (2023)
Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf
AutoML Two-Sample Test. CoRR (2022)
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. CoRR (2022)
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
A Witness Two-Sample Test. AISTATS (2022)
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Niki Kilbertus, Yucen Luo
Learning Counterfactually Invariant Predictors. CoRR (2022)
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML (2022)
Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf
AutoML Two-Sample Test. NeurIPS (2022)
Simon Föll, Alina Dubatovka, Eugen Ernst, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet
Gated Domain Units for Multi-source Domain Generalization. CoRR (2022)
Krikamol Muandet
Impossibility of Collective Intelligence. CoRR (2022)
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
On the Relationship Between Explanation and Prediction: A Causal View. CoRR (2022)
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
An Optimal Witness Function for Two-Sample Testing. CoRR (2021)
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML (2021)
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR (2021)
Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. CoRR (2021)
Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi
Instrument Space Selection for Kernel Maximum Moment Restriction. CoRR (2021)
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat
Counterfactual Mean Embeddings. J. Mach. Learn. Res. 22 (2021)
Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML (2021)
Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
Fair Decisions Despite Imperfect Predictions. AISTATS (2020)
Stefan Klus, Ingmar Schuster, Krikamol Muandet
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. J. Nonlinear Sci. 30 (1) (2020)
Junhyung Park, Krikamol Muandet
Regularised Least-Squares Regression with Infinite-Dimensional Output Space. CoRR (2020)
Junhyung Park, Krikamol Muandet
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. CoRR (2020)
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet
Kernel Conditional Density Operators. AISTATS (2020)
Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler
Kernel Conditional Moment Test via Maximum Moment Restriction. CoRR (2020)
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J. Black, Krikamol Muandet, Siyu Tang
Grasping Field: Learning Implicit Representations for Human Grasps. 3DV (2020)
Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning. NeurIPS (2020)
Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
Dual Instrumental Variable Regression. NeurIPS (2020)
Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler
Kernel Conditional Moment Test via Maximum Moment Restriction. UAI (2020)
Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet
Maximum Moment Restriction for Instrumental Variable Regression. CoRR (2020)
Junhyung Park, Krikamol Muandet
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. NeurIPS (2020)
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J. Black, Krikamol Muandet, Siyu Tang
Grasping Field: Learning Implicit Representations for Human Grasps. CoRR (2020)
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
Learning Kernel Tests Without Data Splitting. CoRR (2020)
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
Learning Kernel Tests Without Data Splitting. NeurIPS (2020)
Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj
Dual IV: A Single Stage Instrumental Variable Regression. CoRR (2019)
Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. CoRR (2019)
Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang
Low-rank Random Tensor for Bilinear Pooling. CoRR (2019)
Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet
Private Causal Inference using Propensity Scores. CoRR (2019)
Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf
Quantum Mean Embedding of Probability Distributions. CoRR (2019)
Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
Improving Consequential Decision Making under Imperfect Predictions. CoRR (2019)
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet
Kernel Conditional Density Operators. CoRR (2019)
Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. CoRR (2019)
Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann
Local Temporal Bilinear Pooling for Fine-Grained Action Parsing. CVPR (2019)
Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann
Local Temporal Bilinear Pooling for Fine-grained Action Parsing. CoRR (2018)
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat
Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference. CoRR (2018)
Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
Design and Analysis of the NIPS 2016 Review Process. J. Mach. Learn. Res. 19 (2018)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
Kernel Mean Embedding of Distributions: A Review and Beyond. Found. Trends Mach. Learn. 10 (1-2) (2017)
Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet
Minimax Estimation of Kernel Mean Embeddings. J. Mach. Learn. Res. 18 (2017)
Stefan Klus, Ingmar Schuster, Krikamol Muandet
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. CoRR (2017)
Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
Design and Analysis of the NIPS 2016 Review Process. CoRR (2017)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf
Kernel Mean Embedding of Distributions: A Review and Beyonds. CoRR (2016)
Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf
Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. 17 (2016)
Rohit Babbar, Krikamol Muandet, Bernhard Schölkopf
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. SDM (2016)
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin
Towards a Learning Theory of Cause-Effect Inference. ICML (2015)
David Lopez-Paz, Krikamol Muandet, Benjamin Recht
The Randomized Causation Coefficient. J. Mach. Learn. Res. 16 (2015)
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR (2015)
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters
Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. 25 (4) (2015)
Krikamol Muandet, Bharath K. Sriperumbudur, Bernhard Schölkopf
Kernel Mean Estimation via Spectral Filtering. NIPS (2014)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
Kernel Mean Shrinkage Estimators. CoRR (2014)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
Kernel Mean Estimation and Stein Effect. ICML (2014)
Krikamol Muandet, Bernhard Schölkopf
One-Class Support Measure Machines for Group Anomaly Detection. CoRR (2014)
Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf
A Permutation-Based Kernel Conditional Independence Test. UAI (2014)
Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
Kernel Mean Estimation and Stein's Effect. CoRR (2013)
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang
Domain Adaptation under Target and Conditional Shift. ICML (3) (2013)
Krikamol Muandet, Bernhard Schölkopf
One-Class Support Measure Machines for Group Anomaly Detection. UAI (2013)
Krikamol Muandet, Bernhard Schölkopf
One-Class Support Measure Machines for Group Anomaly Detection CoRR (2013)
Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
Domain Generalization via Invariant Feature Representation CoRR (2013)
Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
Domain Generalization via Invariant Feature Representation. ICML (1) (2013)
Krikamol Muandet
Hilbert Space Embedding for Dirichlet Process Mixtures CoRR (2012)
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf
Learning from Distributions via Support Measure Machines. NIPS (2012)
Krikamol Muandet, Bernhard Schölkopf, Kenji Fukumizu, Francesco Dinuzzo
Learning from Distributions via Support Measure Machines CoRR (2012)
Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee
Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification. ACML (2009)
Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee
Robust Graph Hyperparameter Learning for Graph Based Semi-supervised Classification. PAKDD (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Krikamol Muandet