| Fast and scalable bayesian deep learning by weight-perturbation in adam M Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava International Conference on Machine Learning, 2611-2620, 2018 | 102 | 2018 |
| Smarper: Context-aware and automatic runtime-permissions for mobile devices K Olejnik, I Dacosta, JS Machado, K Huguenin, ME Khan, JP Hubaux 2017 IEEE Symposium on Security and Privacy (SP), 1058-1076, 2017 | 76 | 2017 |
| Variational bounds for mixed-data factor analysis MEE Khan, G Bouchard, KP Murphy, BM Marlin Advances in Neural Information Processing Systems 23, 1108-1116, 2010 | 65 | 2010 |
| An expectation-maximization algorithm based Kalman smoother approach for event-related desynchronization (ERD) estimation from EEG ME Khan, DN Dutt IEEE transactions on biomedical engineering 54 (7), 1191-1198, 2007 | 56 | 2007 |
| Practical Deep Learning with Bayesian Principles K Osawa, S Swaroop, A Jain, R Eschenhagen, RE Turner, R Yokota, ... arXiv preprint arXiv:1906.02506, 2019 | 53 | 2019 |
| A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. ME Khan, S Mohamed, BM Marlin, KP Murphy AISTATS, 610-618, 2012 | 50 | 2012 |
| Conjugate-computation variational inference: Converting variational inference in non-conjugate models to inferences in conjugate models M Khan, W Lin Artificial Intelligence and Statistics, 878-887, 2017 | 47 | 2017 |
| Piecewise bounds for estimating Bernoulli-logistic latent Gaussian models BM Marlin, ME Khan, KP Murphy ICML, 2011 | 40 | 2011 |
| Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models ME Khan, A Aravkin, M Friedlander, M Seeger International conference on Machine learning, 2013 | 35 | 2013 |
| Kullback-Leibler Proximal Variational Inference ME Khan, P Baqué, F Fleuret, P Fua Advances in Neural Information Processing Systems, 2015 | 32 | 2015 |
| Variational Message Passing with Structured Inference Networks W Lin, N Hubacher, ME Khan arXiv preprint arXiv:1803.05589, 2018 | 31 | 2018 |
| Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression. ME Khan, S Mohamed, KP Murphy NIPS, 3149-3157, 2012 | 31 | 2012 |
| Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions ME Khan, L Switzerland, R Babanezhad, W Lin, M Schmidt, M Sugiyama Uncertainty in Artificial Intelligence (UAI), 2016 | 29 | 2016 |
| Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient A Mishkin, F Kunstner, D Nielsen, M Schmidt, ME Khan Advances in Neural Information Processing Systems, 6248-6258, 2018 | 27 | 2018 |
| Accelerating Bayesian structural inference for non-decomposable Gaussian graphical models B Moghaddam, E Khan, KP Murphy, BM Marlin Advances in Neural Information Processing Systems 22, 1285-1293, 2009 | 24 | 2009 |
| UAVs using Bayesian Optimization to Locate WiFi Devices M Carpin, S Rosati, ME Khan, B Rimoldi arXiv preprint arXiv:1510.03592, 2015 | 21 | 2015 |
| Fast yet simple natural-gradient descent for variational inference in complex models ME Khan, D Nielsen 2018 International Symposium on Information Theory and Its Applications …, 2018 | 20 | 2018 |
| Variational learning for latent Gaussian model of discrete data M Khan University of British Columbia, 2012 | 15 | 2012 |
| TD-Regularized Actor-Critic Methods S Parisi, V Tangkaratt, J Peters, ME Khan arXiv preprint arXiv:1812.08288, 2018 | 14 | 2018 |
| Bayesian Inference ME Khan | 14* | 2018 |
Wu LinUniversity of British ColumbiaVerified email at cs.ubc.ca
