Office: N4.002

Max-Planck-Ring 4

72076 Tübingen

Germany

Max-Planck-Ring 4

72076 Tübingen

Germany

7071601559

krikamol.muandet

My current research aims to develop machine learning techniques that will **bridge the gap between randomized experiments and empirical inference**, enabling machines to better learn causality from data. It has numerous applications in observational studies, medical diagnosis, economics, and online advertisement, for example. To this end, I am employing tools and analyses from related disciplines including but not limited to

- Statistical learning theory
- Kernels and reproducing kernel Hilbert spaces (RKHSs)
- Hilbert space embedding of probability distributions
- Potential outcome framework and Rubin's causal model

In general, I aim to address the most fundamental problems in machine learning and to leverage such insights in solving real-world problems in related disciplines. You can find more information about me and my work at http://krikamol.org.

Kernel methods Observational studies Causal inference RKHS

These are examples of projects I have been working on.

- Counterfactual mean embedding
- Counterfactual policy gradient for observational studies
- Randomization via generalization

My full research statement can also be found at http://krikamol.org/krikamol-research.pdf.

A Hilbert space embedding of distributions (KME)---which generalizes the feature map of individual points to probability measures---has emerged as a powerful machinery for probabilistic modeling, machine learning, and causal discovery. The idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RK...

Bernhard Schölkopf Ilya Tolstikhin David Lopez-Paz Krikamol Muandet

29 results
(View BibTeX file of all listed publications)

**Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces**
2019 (conference) Submitted

**Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference**
*Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML) at ICML*, July 2018 (conference)

**Design and Analysis of the NIPS 2016 Review Process **
*Journal of Machine Learning Research*, 19(49):1-34, 2018, *equal contribution (article)

**Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference**
*Arxiv e-prints*, arXiv:1805.08845v1 [stat.ML], 2018 (article)

**Local Temporal Bilinear Pooling for Fine-grained Action Parsing**
*arXiv preprint arXiv:1812.01922*, 2018 (article)

**Kernel Mean Embedding of Distributions: A Review and Beyond**
*Foundations and Trends in Machine Learning*, 10(1-2):1-141, 2017 (article)

**Minimax Estimation of Kernel Mean Embeddings**
*Journal of Machine Learning Research*, 18(86):1-47, 2017 (article)

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

**Kernel Mean Shrinkage Estimators**
*Journal of Machine Learning Research*, 17(48):1-41, 2016 (article)

**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)

**The Randomized Causation Coefficient**
*Journal of Machine Learning*, 16, pages: 2901-2907, 2015 (article)

**Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations**
*Statistics and Computing *, 25(4):755-766, 2015 (article)

**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)

**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)

**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)

**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)

**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)

**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)

**Domain Generalization via Invariant Feature Representation**
30th International Conference on Machine Learning (ICML2013), 2013 (poster)

**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)

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

**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)

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

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

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

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

**Learning from Distributions via Support Measure Machines**
26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (poster)

**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)

**Hilbert space embedding for Dirichlet Process mixtures**
In NIPS Workshop on confluence between kernel methods and graphical models, 2012 (inproceedings)