RIKEN Center for Advanced Intelligence Project Adaptive Bayesian Intelligence Team
Team Director: Mohammad Emtiyaz Khan (Ph.D.)
Research Summary
Humans and animals have a natural ability to autonomously learn throughout their lives and quickly adapt to their surroundings, but AI systems lack such abilities. Our goal is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with an expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc.
We work on a variety of learning problems, especially those involving supervised, continual, active, federated, online, and reinforcement learning.
Research Subjects:
- Machine Learning
- Statistics
- Artificial Intelligence
Main Research Fields
- Computer Science
Selected Publications
- 1.
Mollenhoff T. and Khan, M. E.
"SAM as an Optimal Relaxation of Bayes"
International Conference on Learning Representation, to appear (2023). - 2.
Khan, M. E. and Swaroop, S.
"Knowledge-adaptation priors"
Advances in Neural Information Processing Systems 34, pp. 19757-19770, (2021). - 3.
Pan, P., Swaroop, S., Immer, A., Eschenhagen, R., Turner, R. and Khan, M. E.
"Continual deep learning by functional regularization of the memorable past"
Advances in Neural Information Processing Systems, to appear (2020). - 4.
Tomasev, N. et al.
"AI For social good: unlocking the opportunity for positive impact"
Nature communications, 11, 2468 (2020). - 5.
Meng, X., Bachmann, R. and Khan, M. E.
"Training binary neural networks using the Bayesian Learning rule"
Proceedings of the 37th International Conference on Machine Learning, PMLR 119, pp. 6852-6861, (2020). - 6.
Osawa, K., Swaroop, S., Khan, M. E., Jain, A., Eschenhagen, R., Turner, R. E. and Yokota, R.
"Practical Deep Learning with Bayesian Principles"
Advances in Neural Information Processing Systems 32, pp. 4287?4299 (2019). - 7.
Khan, M. E., Immer, A., Abedi, E. and Korzepa, M.
"Approximate Inference Turns Deep Networks into Gaussian Processes"
Advances in Neural Information Processing Systems 32, pp. 3094?3104 (2019). - 8.
Cherief-Abdellatif, B.-E., Alquier, P. and Khan, M. E.
"A Generalization Bound for Online Variational Inference"
Proceedings of The 11th Asian Conference on Machine Learning, PMLR 101, pp. 662-677 (2019). - 9.
Lin, W., Khan, M. E. and Schmidt, M.
"Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"
Proceedings of the 36th International Conference on Machine Learning, PMLR 97, pp. 3992-4002 (2019). - 10.
Khan, M. E. and Lin, W.
"Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models"
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54, pp. 878-887 (2017).
Related Links
Lab Members
Principal investigator
- Mohammad Emtiyaz Khan
- Team Director
Core members
- Thomas Moellenhoff
- Senior Research Scientist
- Keigo Nishida
- Special Postdoctoral Researcher
- Hugo Monzon
- Postdoctoral Researcher
- Christopher Anders
- Postdoctoral Researcher
- Yohan Jung
- Postdoctoral Researcher
- Pierre Alquier
- Visiting Scientist
- Rio Yokota
- Visiting Scientist
- Geoffrey Wolfer
- Visiting Scientist
- Robert Minut
- Student Trainee
- Guiomar Laura Pescador Barrios
- Student Trainee
- Henrique Da Silva Gameiro
- Student Trainee
- Giulia Lanzillotta
- Student Trainee
- Anita Yang
- Research Part-time Worker I
- Bai Cong
- Research Part-time Worker I
- Eiki Shimizu
- Research Part-time Worker I
Careers
| Position | Deadline |
|---|---|
| Seeking a Research Scientist or a Postdoctoral Researcher (W24162) | Open until filled |
Contact Information
Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi,
Chuo-ku, Tokyo
103-0027, Japan
Email: emtiyaz.khan@riken.jp