Krikamol Muandet

According to our database, Krikamol Muandet authored at least 63 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Learning Counterfactually Invariant Predictors.

Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus

Trans. Mach. Learn. Res., 2024

Domain Generalisation via Imprecise Learning.

Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Looping in the Human: Collaborative and Explainable Bayesian Optimization.

Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Causal Strategic Learning with Competitive Selection.

Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Gated Domain Units for Multi-source Domain Generalization.

Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet

Trans. Mach. Learn. Res., 2023

Looping in the Human: Collaborative and Explainable Bayesian Optimization.

Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau

CoRR, 2023

Fast Adaptive Test-Time Defense with Robust Features.

Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar

CoRR, 2023

A Measure-Theoretic Axiomatisation of Causality.

Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models.

Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Relationship Between Explanation and Prediction: A Causal View.

Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim

Proceedings of the International Conference on Machine Learning, 2023

Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes.

Junhyung Park, Krikamol Muandet

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Gated Domain Units for Multi-source Domain Generalization.

Simon Föll, Alina Dubatovka, Eugen Ernst, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet

CoRR, 2022

Impossibility of Collective Intelligence.

Krikamol Muandet

CoRR, 2022

AutoML Two-Sample Test.

Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions.

Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf

Proceedings of the International Conference on Machine Learning, 2022

A Witness Two-Sample Test.

Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Counterfactual Mean Embeddings.

Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

J. Mach. Learn. Res., 2021

Instrument Space Selection for Kernel Maximum Moment Restriction.

Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi

CoRR, 2021

An Optimal Witness Function for Two-Sample Testing.

Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

CoRR, 2021

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

Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

Proceedings of the 38th International Conference on Machine Learning, 2021

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction.

Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces.

Stefan Klus, Ingmar Schuster, Krikamol Muandet

J. Nonlinear Sci., 2020

Regularised Least-Squares Regression with Infinite-Dimensional Output Space.

Junhyung Park, Krikamol Muandet

CoRR, 2020

Maximum Moment Restriction for Instrumental Variable Regression.

Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet

CoRR, 2020

Kernel Conditional Moment Test via Maximum Moment Restriction.

Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings.

Junhyung Park, Krikamol Muandet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Dual Instrumental Variable Regression.

Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Kernel Tests Without Data Splitting.

Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning.

Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Kernel Conditional Density Operators.

Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Fair Decisions Despite Imperfect Predictions.

Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Grasping Field: Learning Implicit Representations for Human Grasps.

Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J. Black, Krikamol Muandet, Siyu Tang

Proceedings of the 8th International Conference on 3D Vision, 2020

2019

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

Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf

CoRR, 2019

Dual IV: A Single Stage Instrumental Variable Regression.

Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj

CoRR, 2019

Low-rank Random Tensor for Bilinear Pooling.

Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang

CoRR, 2019

Quantum Mean Embedding of Probability Distributions.

Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf

CoRR, 2019

Private Causal Inference using Propensity Scores.

Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet

CoRR, 2019

Improving Consequential Decision Making under Imperfect Predictions.

Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

CoRR, 2019

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces.

Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

CoRR, 2019

Local Temporal Bilinear Pooling for Fine-Grained Action Parsing.

Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Design and Analysis of the NIPS 2016 Review Process.

Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg

J. Mach. Learn. Res., 2018

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference.

Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

CoRR, 2018

2017

Minimax Estimation of Kernel Mean Embeddings.

Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet

J. Mach. Learn. Res., 2017

Kernel Mean Embedding of Distributions: A Review and Beyond.

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf

Found. Trends Mach. Learn., 2017

2016

Kernel Mean Shrinkage Estimators.

Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf

J. Mach. Learn. Res., 2016

Kernel Mean Embedding of Distributions: A Review and Beyonds.

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf

CoRR, 2016

TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification.

Rohit Babbar, Krikamol Muandet, Bernhard Schölkopf

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

2015

From Points to Probability Measures: Statistical Learning on Distributions with Kernel Mean Embedding.

Krikamol Muandet

PhD thesis, 2015

Computing functions of random variables via reproducing kernel Hilbert space representations.

Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters

Stat. Comput., 2015

The Randomized Causation Coefficient.

David Lopez-Paz, Krikamol Muandet, Benjamin Recht

J. Mach. Learn. Res., 2015

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations.

Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters

CoRR, 2015

Towards a Learning Theory of Cause-Effect Inference.

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

A Permutation-Based Kernel Conditional Independence Test.

Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Kernel Mean Estimation via Spectral Filtering.

Krikamol Muandet, Bharath K. Sriperumbudur, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Kernel Mean Estimation and Stein Effect.

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Kernel Mean Estimation and Stein's Effect.

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf

CoRR, 2013

One-Class Support Measure Machines for Group Anomaly Detection.

Krikamol Muandet, Bernhard Schölkopf

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Domain Adaptation under Target and Conditional Shift.

Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang

Proceedings of the 30th International Conference on Machine Learning, 2013

Domain Generalization via Invariant Feature Representation.

Krikamol Muandet, David Balduzzi, Bernhard Schölkopf

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Hilbert Space Embedding for Dirichlet Process Mixtures

Krikamol Muandet

CoRR, 2012

Learning from Distributions via Support Measure Machines.

Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2009

Robust Graph Hyperparameter Learning for Graph Based Semi-supervised Classification.

Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2009

Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification.

Krikamol Muandet, Sanparith Marukatat, Cholwich Nattee

Proceedings of the Advances in Machine Learning, 2009