Emtiyaz Khan's Publications

2017

Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models,
(AIstats 2017) M.E. Khan and Wu Lin
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
Gaussian-Process-Based Emulators for Building Performance Simulation,
(Building Simulation 2017) P. Rastogi M.E. Khan and M. Anderson

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
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
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

2015

Kullback-Leibler Proximal Variational Inference,
(NIPS 2015) M.E. Khan, P. Baque, F. Fleuret, P. Fua.
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
UAVs using Bayesian Optimization to Locate WiFi Devices,
(NIPS 2015, BayesOpt Workshop) M. Carpin, S. Rosati, M.E. Khan, B. Rimoldi

2014

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

2013

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

2012

Variational Learning for Latent Gaussian Models of Discrete Data,
(PhD thesis) M.E. Khan [ 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

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 [ video ] [ 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 ]

Emtiyaz Khan’s Publications

2017

2016

2015

2014

2013

2011

2010

2009

Before 2008

Writings and Presentations by Emtiyaz Khan