Centers & Labs

RIKEN Center for Advanced Intelligence Project

Approximate Bayesian Inference Team

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

Our goal is to understand fundamental principles of learning from data, and to develop algorithms that can learn like living beings. Currenlty, we focus on the following two sub-goals: (1) to develop Bayesian models to learn from “nasty” data, e.g. data that are unreliable, noisy, high-dimensional, heterogenous, missing, and/or large, (2) to develop algorithms that are accurate, fast, scalable, and easy to use, all at the same time.

Main Research Field

Computer Science

Research Subjects

  • Machine Learning
  • Statistics
  • Artificial Intelligence

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.
  1. W. Lin, N. Hubacher, and M.E. Khan.:
    “Variational Message Passing with Structured Inference Networks”
    International Conference on Learning Representations(ICLR), (2018).
  2. H. Ding, M.E. Khan, I. sato, M. Sugiyama.:
    “Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling”
    Artificial Intelligence and Statistics(AISTATS), (2018).
  3. M.E. Khan and W. Lin.:
    “Conjugate-Computation Variational Inference.: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models”
    Artificial Intelligence and Statistics(AISTATS), (2017).
  4. K. Olejnik, I. I. Dacosta Petrocelli, J. C. Soares Machado, K. Huguenin, M.E. Khan, and J.-P. Hubaux.:
    “SmarPer.: Context-Aware and Automatic Runtime-Permissions for Mobile Devices”
    38th IEEE Symposium on Security and Privacy (S&P), (2017).
  5. P. Rastogi M.E. Khan and M. Anderson.:
    “Gaussian-Process-Based Emulators for Building Performance Simulation”
    Building Simulation, (2017)
  6. *M.E. KHAN, R. BABANEZHAD, W. LIN, M. SCHMIDT, M. SUGIYAMA.:
    “Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions0”
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI), (2016).
  7. *M.E. KHAN, P. BAQUE, F. FLEURET, P. FUA.:
    “Kullback-Leibler Proximal Variational Inference”
    NEURAL INFORMATION PROCESSING (NIPS), (2015).
  8. *M.E. KHAN , A. ARAVKIN, M. FRIEDLANDER, M. SEEGER.:
    “Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models”
    INTERNATIONAL CONFERENCE OF MACHINE LEARNING (ICML), (2013).
  9. *M.E. KHAN , S. MOHAMED, K. MURPHY.:
    “Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression”
    NEURAL INFORMATION PROCESSING (NIPS), (2012).
  10. *B. MARLIN, M. E. KHAN, K. MURPHY.:
    “Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models”
    INTERNATIONAL CONFERENCE OF MACHINE LEARNING (ICML), (2011).