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RIKEN Center for Advanced Intelligence Project Adaptive Bayesian Intelligence Team

Team Director: Mohammad Emtiyaz Khan (Ph.D.)

Research Summary

Mohammad Emtiyaz  Khan(Ph.D.)

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
Florian Seligmann
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

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