| Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam ME Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava arXiv preprint arXiv:1806.04854, 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 ME Khan, G Bouchard, KP Murphy, BM Marlin Advances in Neural Information Processing Systems, 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 | 52 | 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 ME Khan, W Lin arXiv preprint arXiv:1703.04265, 2017 | 47 | 2017 |
| Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. BM Marlin, ME Khan, KP Murphy ICML, 633-640, 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 |
| Fast Bayesian inference for non-conjugate Gaussian process regression E Khan, S Mohamed, KP Murphy Advances in Neural Information Processing Systems, 3140-3148, 2012 | 31 | 2012 |
| Variational Message Passing with Structured Inference Networks W Lin, N Hubacher, ME Khan arXiv preprint arXiv:1803.05589, 2018 | 30 | 2018 |
| 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, 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
