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.
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
Workshop Organizations
Click here to download a full CV.
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)
Tolstikhin, I., Sriperumbudur, B., Muandet, K.
Minimax Estimation of Kernel Mean Embeddings
Journal of Machine Learning Research, 18, pages: 1-47, 2017 (article) To be published
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)
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)
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)
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)
Lopez-Paz, D., Muandet, K., Recht, B.
The Randomized Causation Coefficient
Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)
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)
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)
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)
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)
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)
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)
Muandet, K., Balduzzi, D., Schölkopf, B.
Domain Generalization via Invariant Feature Representation
30th International Conference on Machine Learning (ICML2013), 2013 (poster)
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)
Muandet, K., Schölkopf, B.
One-class Support Measure Machines for Group Anomaly Detection
29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)
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)
Muandet, K.
Domain Generalization via Invariant Feature Representation
30th International Conference on Machine Learning (ICML2013), 2013 (talk)
Muandet, K.
Support Measure Machines for Quasar Target Selection
Astro Imaging Workshop, 2012 (talk)
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)
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)
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)
Muandet, K.
Hilbert Space Embedding for Dirichlet Process Mixtures
NIPS Workshop on Confluence between Kernel Methods and Graphical Models, December 2012 (talk)
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