Krikamol Muandet

Alumni

Empirical Inference

I am currently a research scientist at the Empirical Inference Department of Max Planck Institute for Intelligent Systems in Tübingen, Germany, working with Prof. Bernhard Schölkopf. You are also encouraged to visit my personal webpage http://krikamol.org for further information about my works and research. I also write an academic blog occasionally about machine learning at http://krikamol.org/mlblog.

My research interest lies in use of machine learning techniques to advance our understanding of real-world phenomena and to solve various difficult problems, e.g., classification and regression problems. I am currently employing kernel methods to deal with probability distributions. The list below highlights some of my research interest.

Learning from probability measures with kernels
Hilbert space embedding for distributions and stochastic processes
Bayesian nonparametrics and approximate inference
Kernel methods and random measures
Junction between Bayesian inference and kernel methods.
Causal inference and counterfactual analysis
Philosophy of Causality
etc.

I am also coordinating the Empirical Inference Journal Club (EIJC). Please visit http://krikamol.org/misc/eijc/ for more information.

Support Measure Machines
A generalization of support vector machines to a space of probability measures.

Domain Generalization and Prediction
How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains?

Stein's Phenomenon in Reproducing Kernel Hilbert Space
Investigate a controversial phenomenon known as "Stein's phenomenon" in the reproducing kernel Hilbert space (RKHS).

Educations

B.Sc. in Computer Science (First Class Honor), Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand
M.Sc. in Machine Learning (Distinction), University College London, London, United Kingdom
Ph.D. in Machine Learning, Max Planck Institute for Intelligent Systems, Tübingen, Germany

Workshop Organizations

Co-organizer, Empirical Inference Symposium, in honor of the 75th birthday anniversary of Professor Vladimir N. Vapnik. December 8-10, 2011. Max Planck Institute for Intelligent Systems, Tuebingen, Germany.

Click here to download a full CV.

26 results (View BibTeX file of all listed publications)

2018

Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 2018, *equal contribution (article) Accepted

arXiv [BibTex]

2018

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U. Design and Analysis of the NIPS 2016 Review Process Journal of Machine Learning Research, 2018, *equal contribution (article) Accepted

arXiv [BibTex]

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract

This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

arXiv [BibTex]

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S. Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

arXiv [BibTex]

2017

Minimax Estimation of Kernel Mean Embeddings

Tolstikhin, I., Sriperumbudur, B., Muandet, K.

Journal of Machine Learning Research, 18(86):1-47, 2017 (article)

link (url) [BibTex]

2017

Tolstikhin, I., Sriperumbudur, B., Muandet, K. Minimax Estimation of Kernel Mean Embeddings Journal of Machine Learning Research, 18(86):1-47, 2017 (article)

link (url) [BibTex]

Kernel Mean Embedding of Distributions: A Review and Beyond

Muandet, K., Fukumizu, K., Sriperumbudur, B., Schölkopf, B.

Foundations and Trends in Machine Learning, 10(1-2):1-141, 2017 (article)

DOI [BibTex]

Muandet, K., Fukumizu, K., Sriperumbudur, B., Schölkopf, B. Kernel Mean Embedding of Distributions: A Review and Beyond Foundations and Trends in Machine Learning, 10(1-2):1-141, 2017 (article)

DOI [BibTex]

2016

Kernel Mean Shrinkage Estimators

Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B.

Journal of Machine Learning Research, 17(48):1-41, 2016 (article)

link (url) Project Page [BibTex]

2016

Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B. Kernel Mean Shrinkage Estimators Journal of Machine Learning Research, 17(48):1-41, 2016 (article)

link (url) Project Page [BibTex]

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

Babbar, R., Muandet, K., Schölkopf, B.

Proceedings of the 2016 SIAM International Conference on Data Mining, pages: 234-242, (Editors: Sanjay Chawla Venkatasubramanian and Wagner Meira), SDM, 2016 (conference)

DOI [BibTex]

Babbar, R., Muandet, K., Schölkopf, B. TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification Proceedings of the 2016 SIAM International Conference on Data Mining, pages: 234-242, (Editors: Sanjay Chawla Venkatasubramanian and Wagner Meira), SDM, 2016 (conference)

DOI [BibTex]

2015

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

Muandet, K.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

[BibTex]

2015

Muandet, K. From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

[BibTex]

The Randomized Causation Coefficient

Lopez-Paz, D., Muandet, K., Recht, B.

Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)

link (url) Project Page [BibTex]

Lopez-Paz, D., Muandet, K., Recht, B. The Randomized Causation Coefficient Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)

link (url) Project Page [BibTex]

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations

Schölkopf, B., Muandet, K., Fukumizu, K., Harmeling, S., Peters, J.

Statistics and Computing , 25(4):755-766, 2015 (article)

DOI Project Page [BibTex]

Schölkopf, B., Muandet, K., Fukumizu, K., Harmeling, S., Peters, J. Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations Statistics and Computing , 25(4):755-766, 2015 (article)

DOI Project Page [BibTex]

Towards a Learning Theory of Cause-Effect Inference

Lopez-Paz, D., Muandet, K., Schölkopf, B., Tolstikhin, I.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1452–1461, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

Web Project Page [BibTex]

Lopez-Paz, D., Muandet, K., Schölkopf, B., Tolstikhin, I. Towards a Learning Theory of Cause-Effect Inference In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1452–1461, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

Web Project Page [BibTex]

2014

A Permutation-Based Kernel Conditional Independence Test

Doran, G., Muandet, K., Zhang, K., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

PDF Project Page [BibTex]

2014

Doran, G., Muandet, K., Zhang, K., Schölkopf, B. A Permutation-Based Kernel Conditional Independence Test In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

PDF Project Page [BibTex]

Kernel Mean Estimation via Spectral Filtering

Muandet, K., Sriperumbudur, B., Schölkopf, B.

In Advances in Neural Information Processing Systems 27, pages: 1-9, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) Project Page [BibTex]

Muandet, K., Sriperumbudur, B., Schölkopf, B. Kernel Mean Estimation via Spectral Filtering In Advances in Neural Information Processing Systems 27, pages: 1-9, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) Project Page [BibTex]

Single-Source Domain Adaptation with Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

Project Page [BibTex]

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C. Single-Source Domain Adaptation with Target and Conditional Shift In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

Project Page [BibTex]

Kernel Mean Estimation and Stein Effect

Muandet, K., Fukumizu, K., Sriperumbudur, B., Gretton, A., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 10-18, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF Project Page [BibTex]

Muandet, K., Fukumizu, K., Sriperumbudur, B., Gretton, A., Schölkopf, B. Kernel Mean Estimation and Stein Effect In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 10-18, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF Project Page [BibTex]

2013

Domain adaptation under Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

PDF Web Project Page [BibTex]

2013

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z. Domain adaptation under Target and Conditional Shift In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

PDF Web Project Page [BibTex]

Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

30th International Conference on Machine Learning (ICML2013), 2013 (poster)

PDF [BibTex]

Muandet, K., Balduzzi, D., Schölkopf, B. Domain Generalization via Invariant Feature Representation 30th International Conference on Machine Learning (ICML2013), 2013 (poster)

PDF [BibTex]

One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

In Proceedings 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 449-458, (Editors: Ann Nicholson and Padhraic Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

PDF Project Page [BibTex]

Muandet, K., Schölkopf, B. One-class Support Measure Machines for Group Anomaly Detection In Proceedings 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 449-458, (Editors: Ann Nicholson and Padhraic Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

PDF Project Page [BibTex]

One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)

PDF [BibTex]

Muandet, K., Schölkopf, B. One-class Support Measure Machines for Group Anomaly Detection 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)

PDF [BibTex]

Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 (inproceedings)

Web Project Page [BibTex]

Muandet, K., Balduzzi, D., Schölkopf, B. Domain Generalization via Invariant Feature Representation In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 (inproceedings)

Web Project Page [BibTex]

Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

PDF [BibTex]

Muandet, K. Domain Generalization via Invariant Feature Representation 30th International Conference on Machine Learning (ICML2013), 2013 (talk)

PDF [BibTex]

2012

Support Vector Machines, Support Measure Machines, and Quasar Target Selection

Muandet, K.

Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

[BibTex]

2012

Muandet, K. Support Vector Machines, Support Measure Machines, and Quasar Target Selection Center for Cosmology and Particle Physics (CCPP), New York University, December 2012 (talk)

[BibTex]

Hilbert Space Embedding for Dirichlet Process Mixtures

Muandet, K.

NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

[BibTex]

Muandet, K. Hilbert Space Embedding for Dirichlet Process Mixtures NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)

[BibTex]

Support Measure Machines for Quasar Target Selection

Muandet, K.

Astro Imaging Workshop, 2012 (talk)

Abstract

In this talk I will discuss the problem of quasar target selection. The objects attributes in astronomy such as fluxes are often subjected to substantial and heterogeneous measurement uncertainties, especially for the medium-redshift between 2.2 and 3.5 quasars which is relatively rare and must be targeted down to g ~ 22 mag. Most of the previous works for quasar target selection includes UV-excess, kernel density estimation, a likelihood approach, and artificial neural network cannot directly deal with the heterogeneous input uncertainties. Recently, extreme deconvolution (XD) has been used to tackle this problem in a well-posed manner. In this work, we present a discriminative approach for quasar target selection that can deal with input uncertainties directly. To do so, we represent each object as a Gaussian distribution whose mean is the object's attribute vector and covariance is the given flux measurement uncertainty. Given a training set of Gaussian distributions, the support measure machines (SMMs) algorithm are trained and used to build the quasar targeting catalog. Preliminary results will also be presented. Joint work with Jo Bovy and Bernhard Sch{\"o}lkopf

Web [BibTex]

Muandet, K. Support Measure Machines for Quasar Target Selection Astro Imaging Workshop, 2012 (talk)

Web [BibTex]

Learning from Distributions via Support Measure Machines

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B.

26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (poster)

PDF [BibTex]

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B. Learning from Distributions via Support Measure Machines 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (poster)

PDF [BibTex]

Learning from distributions via support measure machines

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 10-18, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF Project Page [BibTex]

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B. Learning from distributions via support measure machines In Advances in Neural Information Processing Systems 25, pages: 10-18, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF Project Page [BibTex]

Hilbert space embedding for Dirichlet Process mixtures

Muandet, K.

In NIPS Workshop on confluence between kernel methods and graphical models, 2012 (inproceedings) To be published

Project Page [BibTex]

Muandet, K. Hilbert space embedding for Dirichlet Process mixtures In NIPS Workshop on confluence between kernel methods and graphical models, 2012 (inproceedings) To be published

Project Page [BibTex]