Early Drafts/Preprints

• The Bayesian Learning Rule,
  (Preprint) M.E. Khan, H. Rue [ arXiv ] [ Tweet ]
  
• Beyond Target Networks: Improving Deep Q-learning with Functional Regularization,
  (Preprint) A. Piché, J. Marino, G. M. Marconi, C. Pal, M.E. Khan [ ArXiv ]

2021

• Knowledge-Adaptation Priors,
  (NeurIPS 2021) M.E. Khan, S. Swaroop [ arXiv ] [ Slides ] [ Tweet ] [ SlidesLive Video ]
• Dual Parameterization of Sparse Variational Gaussian Processes,
  (NeurIPS 2021) P. Chang, V. ADAM, M.E. Khan, A. Solin [ arXiv ]
• Subset-of-Data Variational Inference for Deep Gaussian-Process Regression,
  (UAI 2021) A. Jain, P.K. Srijith, M.E. Khan [ arXiv ] [ Code ]
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning,
  (ICML 2021) A. Immer, M. Bauer, V. Fortuin, G. Ratsch, M.E. Khan, [ arXiv ]
• Tractable structured natural gradient descent using local parameterizations,
  (ICML 2021) W. Lin, F. Nielsen, M.E. Khan, M. Schmidt [ arXiv ]
• Structured second-order methods via natural gradient descent,
  (ICML 2021 Workshop on "Beyond first-order methods in ML systems)
  W. Lin, F. Nielsen, M.E. Khan, M. Schmidt [ arXiv ]

2020

• Learning Algorithms from Bayesian Principles,
  (Draft) M.E. Khan, H. Rue [ Draft version ]
  [ Full version on arXiv ] under a new title "The Bayesian Learning Rule"
• Continual Deep Learning by Functional Regularisation of Memorable Past
  (NeurIPS 2020) P. Pan^*, S. Swaroop^*, A. Immer, R. Eschenhagen, R. E. Turner, M.E. Khan [ arXiv ] [ Code ] [ Poster ]
  Accepted for an oral presentation, 1% of all submisssions (105 out of 9454 submissions),
  
• Fast Variational Learning in State-Space Gaussian Process Models ,
  (MLSP 2020) P. E. Chang, W. J. Wilkinson, M.E. Khan, A. Solin [ arXiv ]
• AI for Social Good: Unlocking the Opportunity for Positive Impact,
  (Nature Communications 2020) with Nenad Tomašev and many others
  [ Paper ] [ Declaration on AI2SG ] [ Dagstuhl AI4SG 2019 ] [ Press Release ]
• Training Binary Neural Networks using the Bayesian Learning Rule,
  (ICML 2020) X. Meng, R. Bachmann, M.E. Khan [ arXiv ] [ Code ]
• Handling the Positive-Definite Constraint in the Bayesian Learning Rule,
  (ICML 2020) W. Lin, M. Schmidt, M.E. Khan [ arXiv ]
• VILD: Variational Imitation Learning with Diverse-quality Demonstrations,
  (ICML 2020) V. Tangkaratt, B. Han, M.E. Khan, M. Sugiyama [ arXiv ]
• Exact Recovery of Low-rank Tensor Decomposition under Reshuffling,
  (AAAI 2020) C. Li, M.E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, Q. Zhao [ arXiv ]

2019

• Practical Deep Learning with Bayesian Principles,
  (NeurIPS 2019) K. Osawa, S. Swaroop, A. Jain, R. Eschenhagen, R.E. Turner, R. Yokota, M.E. Khan. [ arXiv ] [ Code ]
• Approximate Inference Turns Deep Networks into Gaussian Processes,
  (NeurIPS 2019) M.E. Khan, A. Immer, E. Abedi, M. korzepa. [ arXiv ] [ Code ]
• A Generalization Bound for Online Variational Inference (best paper award),
  (ACML 2019) Badr-Eddine Chérief-Abdellatif, Pierre Alquier, M.E. Khan. [ arXiv ]
• Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations,
  (ICML, 2019) W. Lin, M.E. Khan, M. Schmidt. [ arXiv ] [ paper ] [ Appendix ] [ Code ]
  Also appeared at the Symposium on Advances in Approximate Bayesian Inference at NeurIPS 2018 [ Short Paper ]
  
• Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures,
  (ICML workshop on Stein's Method in ML and Stats, 2019) W. Lin, M.E. Khan, M. Schmidt. [ arXiv ]
• Scalable Training of Inference Networks for Gaussian-Process Models,
  (ICML, 2019) J. Shi, M.E. Khan, J. Zhu. [ arXiv ] [ paper ] [ Appendix ] [ Code ]
• TD-Regularized Actor-Critic Methods,
  (Machine Learning, 2019. A short version appeared at EWRL 2018)
  S. Parisi, V. Tangkaratt, J. Peters, M.E. Khan. [ Springer Online Version ] [ arXiv ] [ Short version at EWRL 2018 ]

2018

• SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient,
  (NeurIPS 2018) A. Miskin, F. Kunstner, D. Nielsen, M. Schmidt, M.E. Khan. [ arXiv ] [ Poster] [ 3-min Video] [ Code ]
• Natural Variational Continual Learning,
  (Continual Learning Workshop at NIPS 2018)
  H. Tseran, M.E. Khan , T. Harada, T. Bui [ Paper ].
• Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models,
  (ISITA 2018) M.E. Khan and D. Nielsen, [ arXiv ] [ IEEE explore ] [ Slides ]
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam,
  (ICML 2018) M.E. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, and A. Srivastava, [ arXiv Version ] [ Code ] [ Slides ]
• Variational Message Passing with Structured Inference Networks,
  (ICLR 2018) W. Lin, N. Hubacher, and M.E. Khan, [ Paper ] [ arXiv Version ] [ Code ]
• Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling,
  (AI-Stats 2018) H. Ding, M.E. Khan, I. sato, M. Sugiyama, [ Paper ] [ Appendix ] [ Code ]

2017

• Vprop: Variational Inference using RMSprop,
  (NIPS 2017, Workshop on Bayesian Deep Learning)
  M.E. Khan, Z. Liu, V. Tangkaratt, and Y. Gal [ Workshop version ] [ Poster ]
• Variational Adaptive-Newton Method for Explorative-Learning,
  (NIPS 2017, Workshop on Advances in Approximate Bayesian Inference)
  M.E. Khan, W. Lin, V. Tangkaratt, Z. Liu, and D. Nielsen [ arXiv Version ] [ Poster ]
• Natural-Gradient Stochastic Variational Inference for Non-Conjugate Structured Variational Autoencoder,
  (ICML 2017, Workshop on Deep Structure Prediction) W. Lin, M.E. Khan, N. Hubacher, and D. Nielsen [ Paper ]
• Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models,
  (AIstats 2017) M.E. Khan and W. Lin [ Paper ] [ Code for Logistic Reg + GPs ] [ Code for Correlated Topic Model ]
• SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices,
  (38th IEEE Symposium on Security and Privacy (S&P;), San Jose, CA, USA, May 22-24, 2017)
  K. Olejnik, I. I. Dacosta Petrocelli, J. C. Soares Machado, K. Huguenin, M.E. Khan , and J.-P. Hubaux [ Paper ] [ Code ] [ SmarPer Homepage ]
• Gaussian-Process-Based Emulators for Building Performance Simulation,
  (Building Simulation 2017) P. Rastogi M.E. Khan and M. Anderson [ Paper ] [ Building Simulation Data ] [ Code ]

2016

• Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions,
  (UAI 2016) M.E. Khan, R. Babanezhad, W. Lin, M. Schmidt, M. Sugiyama [ Paper + Appendix ] [ Code ]
• Online Collaborative Prediction of Regional Vote Results,
  (IEE Intenational Conference on Data Science and Advanced analytics (DSAA) 2016)
  V. Etter M.E. Khan, M. Grossglausser, and P. Thiran [ Paper ] [ Code & Data ]
• Variational Inference on Deep Exponential Family by using Variational Inferences on Conjugate Models,
  (Workshop on Bayesian Deep Learning at NIPS 2016)
  M.E. Khan and Wu Lin [ Paper ]

2015

• Kullback-Leibler Proximal Variational Inference,
  (NIPS 2015) M.E. Khan, P. Baque, F. Fleuret, P. Fua. [ Paper ] [ Appendix ]
• Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence,
  (NIPS 2015, Workshop on Advances in Approximate Bayesian Inference)
  M.E. Khan, R. Babanezhad, W. Lin, M. Schmidt, M. Sugiyama [ NIPS workshop version ] [ arXiv ]
• UAVs using Bayesian Optimization to Locate WiFi Devices,
  (NIPS 2015, BayesOpt Workshop) M. Carpin, S. Rosati, M.E. Khan, B. Rimoldi [ NIPS workshop version ] [ arXiv ]

2014

• Decoupled Variational Gaussian Inference,
  (NIPS 2014) M.E. Khan [ Paper and appendix ]
• Variational Gaussian Inference for Bilinear Models of Count Data,
  (ACML 2014) Y.J. Ko, M.E. Khan [ Paper ]
• Scalable collaborative Bayesian preference learning,
  (AI-Stats 2014, NIPS Workshop 2014) M.E. Khan ,Y.J. Ko, M. Seeger [ Paper ] [ Appendix ] [ Talk ]

2013

• Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models,
  (ICML 2013) M.E. Khan , A. Aravkin, M. Friedlander, M. Seeger [ Paper ]

2012

• Variational Learning for Latent Gaussian Models of Discrete Data,
  (PhD thesis) M.E. Khan [ Link to pdf ]
• Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression,
  (NIPS 2012) M.E. Khan , S. Mohamed, K. Murphy [ pdf ] [ poster ] [ code ]
• Large-scale Approximate Bayesian Inference for Exponential Family Latent Gaussian Models,
  (ISBA 2012, poster) M.E. Khan, S. Mohamed, K. Murphy
• A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models,
  (AIstats 2012) M.E. Khan, S. Mohamed, B. Marlin, K. Murphy [ pdf ] [ poster ] [ code ] [ datasets]

2011

• Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models,
  (ICML 2011) B. Marlin, M. E. Khan, K. Murphy [ pdf ] [ presentation ] [ poster ] [ code ] [ appendix ]

2010

• Variational Bounds for Mixed-Data Factor Analysis,
  (NIPS 2010) M. E. Khan, B. Marlin, G. Bouchard, K. Murphy [ pdf ] [ poster ] [ MATLAB code ] [ corrected version ]
  Our implementation for mixture model had a bug, the corrected version contains new results.

2009

• Accelerating Bayesian Structural Inference for Non-decomposable Gaussian Graphical Model,
  (NIPS 2009, Oral) B. Mogaddham, B. Marlin, M. E. Khan, K. Murphy [ pdf ] [ poster ] [ MATLAB code (Ben’s website) ]

Before 2008

• An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG,
  (IEEE Transactions on Biomedical Engineering, 2007) M.E. Khan, D. N. Dutt [ pdf ] [ MATLAB code ]
• State Estimation with Wireless Devices,
  Third Int. Conf. Intelligent Sensing and Information Processing (ICISIP), 2005
  M.E. Khan, H. Raghavan, J. Brahmajosyula, S. K. Ramalingam, S. Narasimhan [ pdf ]
• Hybrid System Framework for State Estimation in Systems with Wireless Devices,
  Annual Meeting of American Institute of Chemical Engineers (AIChe), 2005
  M.E. Khan, H. Raghavan, J. Brahmajosyula, S.K. Ramalingam, S. Narasimhan
• Expectation-Maximization (EM) Algorithm for Instantaneous Frequency Estimation with Kalman Smoother (EUSIPCO 2004) M. E. Khan, D.N. Dutt [ pdf ]
• Estimation of ERS/ERD with Kalman Smoother: An EM Algorithm Approach,
  BIOSIGNAL 2004 M. E. Khan, D. N. Dutt

  Writings and Presentations by Emtiyaz Khan

    Home     Peer-Reviewed Publications

• Variational Methods for Discrete-Data Latent Gaussian Models
  (11 Mar 2012) Invited talks at EPFL, XRCE, and INRIA-SIERRA [ slides ].
• A Tutorial on Approximate Message Passing
  (08 Feb 2012) talk at DNOISE [ Report ].
• Piecewise Bounds for Estimating Discrete-Data Latent Gaussian Models
  (29 Sep 2011) Talk at Microsoft Research, Redmond [ video ] [ slides ]
• Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models
  (29 Jun 2011) Talk at ICML 2011 [ slides ]
• An Expectation-Maximization algorithm for Learning the Latent-Gaussian Model with Gaussian Likelihood
  (22 Apr 2011) Derivation of a simple factor analysis model [ pdf ]
• Variational EM Algorithms for Correlated Topic Models
  (14 Sep 2009) Derivation of Variational EM algorithm for Correlated Topic Model [ pdf ]
• Bayesian Inference for a model with Gaussian likelihood and Gaussian prior on the mean of the likelihood
  (25 Feb 2009) [ pdf ]
• Empirical Bayes estimate of Covariance for Multivariate Normal Distribution
  (29 Jan 2009) [ pdf ]
• Bayesian search algorithms for decomposable Guassian graphical model
  (24 Dec 2008) [ pdf ]
• Updating Inverse of a Matrix when a Column is added/removed
  (27 Feb 2008) [ pdf ] [ code ]
• Kalman Filters
  (25 Feb 2008) Slides from my talk at the Dynamic Programming course at UBC [ Slides ] [ Demo ]
• Matrix Inversion Lemma and Information Filter
  (25 Feb 2008) Deriving information filter by applying matrix inversion lemma to Kalman filters [ pdf ]
• Variational Bayes and Variational Message Passing
  (30 Oct 2007) Presentation at the Machine learning Reading Group at UBC [ slides ]
• Exchangeability, Polya’s Urn, and De-Finetti’s Theorem
  (02 Oct 2007) [ pdf ]
• A review of Linear Algebra
  (28 Sep 2007) Presented for the refresher courses at Computer Science, UBC [ slides ]
• A review of basic probability theory
  (18 Sep 2007) Presented for the refresher courses at Computer Science, UBC [ Slides ]
• Brain-Computer Interface: Overview, Methods, and Opportunities
  (14 June 2007) Talk at the CIFAR Time-series Workshop, University of Toronto [ slides ]
• Talk on Signal Compression and JPEG
  (18 May 2007) Presented at the Undistinguished Lecture Series (UDLS), CS, UBC [ Abstract ] [ slides ]
• Compressed Sensing, Compressed Classification and Joint Signal Recovery
  (April 2007) Course project-report for the Machine Learning course taught by Nando D. Frietas [ pdf ]
• Gibbs Sampling for the Probit Regression Model with Gaussian Markov Random Field Latent Variables
  (April 2007) Project report for the Statistical-Computation course taught by Arnaud Doucet [ pdf ] [ slides ]
• A review of basic probability theory
  (Jan 2007) Presented at the Undistinguished Lecture Series (UDLS), CS, UBC [ Slides ]
• Game theory models for Pursuit-evasion games
  (Dec 2007) Course-project report for the Multi-agent systems course taught by Kevin Leyton-Brown [ pdf ]
• An incremental deployment algorithm for mobile sensors
  (Dec 2007) Course-project report for the Numerical Optimization course taught by Michael Friedlander [ pdf ]
• Decentralized Information Filter
  (2005) [ pdf ]
• Consistency Checks for Linear Gaussian Filters
  (2005) [ pdf ]
• On the Construction of Global Estimate from the Local Estimates in Track Fusion
  (2005) [ pdf ]