Office: N4.002

Max-Planck-Ring 4

72076 Tübingen

Germany

Max-Planck-Ring 4

72076 Tübingen

Germany

+49 7071 601 559

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 *learning algorithm* is the backbone of machine learning that distinguishes it from traditional computer programming by allowing data-driven model building. In the past years, we have developed learning algorithms using a number and tools and for diverse application domains, as outlined below.

Anant Raj Wittawat Jitkrittum Krikamol Muandet Bernhard Schölkopf Giambattista Parascandolo Mateo Rojas-Carulla Rohit Babbar Niki Kilbertus Mehdi S. M. Sajjadi

Machine learning algorithms are designed to generalize from past observations across different problem settings. The goal of learning theory is to analyze statistical and computational properties of learning algorithms and to provide guarantees on their performance. To do so, it poses these tasks in a rigorous mathematical fram...

Ilya Tolstikhin Ruth Urner Carl Johann Simon-Gabriel Matej Balog Adam Scibior Krikamol Muandet Paul Rubenstein Bernhard Schölkopf

29 results
(View BibTeX file of all listed publications)

**Local Temporal Bilinear Pooling for Fine-grained Action Parsing**
In *Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)*, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

**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)

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

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