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Author Information
Mohammad Emtiyaz Khan (RIKEN)
Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also a visiting professor at the Tokyo University of Agriculture and Technology (TUAT). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For the past 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.
More from the Same Authors
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2021 : Beyond Target Networks: Improving Deep $Q$-learning with Functional Regularization »
Alexandre Piche · Joseph Marino · Gian Maria Marconi · Valentin Thomas · Chris Pal · Mohammad Emtiyaz Khan -
2022 : Can Calibration Improve Sample Prioritization? »
Ganesh Tata · Gautham Krishna Gudur · Gopinath Chennupati · Mohammad Emtiyaz Khan -
2022 : Practical Structured Riemannian Optimization with Momentum by using Generalized Normal Coordinates »
Wu Lin · Valentin Duruisseaux · Melvin Leok · Frank Nielsen · Mohammad Emtiyaz Khan · Mark Schmidt -
2022 : Invited Keynote 2 »
Mohammad Emtiyaz Khan · Mohammad Emtiyaz Khan -
2021 Poster: Dual Parameterization of Sparse Variational Gaussian Processes »
Vincent ADAM · Paul Chang · Mohammad Emtiyaz Khan · Arno Solin -
2021 Poster: Knowledge-Adaptation Priors »
Mohammad Emtiyaz Khan · Siddharth Swaroop -
2019 Poster: Approximate Inference Turns Deep Networks into Gaussian Processes »
Mohammad Emtiyaz Khan · Alexander Immer · Ehsan Abedi · Maciej Korzepa -
2019 Poster: Practical Deep Learning with Bayesian Principles »
Kazuki Osawa · Siddharth Swaroop · Mohammad Emtiyaz Khan · Anirudh Jain · Runa Eschenhagen · Richard Turner · Rio Yokota -
2019 Tutorial: Deep Learning with Bayesian Principles »
Mohammad Emtiyaz Khan -
2015 Poster: Kullback-Leibler Proximal Variational Inference »
Mohammad Emtiyaz Khan · Pierre Baque · François Fleuret · Pascal Fua -
2012 Poster: Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression »
Mohammad Emtiyaz Khan · Shakir Mohamed · Kevin Murphy -
2010 Poster: Variational bounds for mixed-data factor analysis »
Mohammad Emtiyaz Khan · Benjamin Marlin · Guillaume Bouchard · Kevin Murphy -
2009 Oral: Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models »
Baback Moghaddam · Benjamin Marlin · Mohammad Emtiyaz Khan · Kevin Murphy -
2009 Poster: Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models »
Baback Moghaddam · Benjamin Marlin · Mohammad Emtiyaz Khan · Kevin Murphy