Mohammad Emtiyaz Khan

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 73

Top Topics

Variational Inference

Gaussian Process

Natural Gradient

Top Venues

Publications

Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan
Model Merging by Uncertainty-Based Gradient Matching. CoRR (2023)
Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. ICML (2023)
Mohammad Emtiyaz Khan
Variational Bayes Made Easy. CoRR (2023)
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
Exploiting Inferential Structure in Neural Processes. UAI (2023)
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
Exploiting Inferential Structure in Neural Processes. CoRR (2023)
Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Lie-Group Bayesian Learning Rule. CoRR (2023)
Paul E. Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan
Memory-Based Dual Gaussian Processes for Sequential Learning. CoRR (2023)
Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Simplifying Momentum-based Riemannian Submanifold Optimization. CoRR (2023)
Eren Mehmet Kiral, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Lie-Group Bayesian Learning Rule. AISTATS (2023)
Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data. CoRR (2023)
Thomas Möllenhoff, Mohammad Emtiyaz Khan
SAM as an Optimal Relaxation of Bayes. ICLR (2023)
Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan
Memory-Based Dual Gaussian Processes for Sequential Learning. ICML (2023)
Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data. NeurIPS (2023)
Thomas Möllenhoff, Mohammad Emtiyaz Khan
SAM as an Optimal Relaxation of Bayes. CoRR (2022)
Alexandre Piché, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan
Bridging the Gap Between Target Networks and Functional Regularization. CoRR (2022)
Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan
Can Calibration Improve Sample Prioritization? CoRR (2022)
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Structured second-order methods via natural gradient descent. CoRR (2021)
Mohammad Emtiyaz Khan, Siddharth Swaroop
Knowledge-Adaptation Priors. CoRR (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML (2021)
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan
Subset-of-data variational inference for deep Gaussian-processes regression. UAI (2021)
Mohammad Emtiyaz Khan, Siddharth Swaroop
Knowledge-Adaptation Priors. NeurIPS (2021)
Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin
Dual Parameterization of Sparse Variational Gaussian Processes. NeurIPS (2021)
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression. CoRR (2021)
Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin
Dual Parameterization of Sparse Variational Gaussian Processes. CoRR (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR (2021)
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Tractable structured natural-gradient descent using local parameterizations. ICML (2021)
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
Tractable structured natural gradient descent using local parameterizations. CoRR (2021)
Mohammad Emtiyaz Khan, Håvard Rue
The Bayesian Learning Rule. CoRR (2021)
Alexandre Piché, Joseph Marino, Gian Maria Marconi, Christopher Joseph Pal, Mohammad Emtiyaz Khan
Beyond Target Networks: Improving Deep Q-learning with Functional Regularization. CoRR (2021)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS (2020)
Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan
Training Binary Neural Networks using the Bayesian Learning Rule. CoRR (2020)
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. ICML (2020)
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
Variational Imitation Learning with Diverse-quality Demonstrations. ICML (2020)
Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. AAAI (2020)
Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, Arno Solin
Fast Variational Learning in State-Space Gaussian Process Models. CoRR (2020)
Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan
Training Binary Neural Networks using the Bayesian Learning Rule. ICML (2020)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR (2020)
Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, Arno Solin
Fast Variational Learning in State-Space Gaussian Process Models. MLSP (2020)
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
Handling the Positive-Definite Constraint in the Bayesian Learning Rule. CoRR (2020)
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Scalable Training of Inference Networks for Gaussian-Process Models. CoRR (2019)
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. ICML (2019)
Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
A Generalization Bound for Online Variational Inference. ACML (2019)
Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Tom Schaul
AI for the Social Good (Dagstuhl Seminar 19082). Dagstuhl Reports 9 (2) (2019)
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
Practical Deep Learning with Bayesian Principles. CoRR (2019)
Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
Practical Deep Learning with Bayesian Principles. NeurIPS (2019)
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
Approximate Inference Turns Deep Networks into Gaussian Processes. NeurIPS (2019)
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Scalable Training of Inference Networks for Gaussian-Process Models. ICML (2019)
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. CoRR (2019)
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR (2019)
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. CoRR (2019)
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
Approximate Inference Turns Deep Networks into Gaussian Processes. CoRR (2019)
Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
TD-regularized actor-critic methods. Mach. Learn. 108 (8-9) (2019)
Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
A Generalization Bound for Online Variational Inference. CoRR (2019)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR (2018)
Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. AISTATS (2018)
Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan
Variational Message Passing with Structured Inference Networks. ICLR (Poster) (2018)
Chao Li, Mohammad Emtiyaz Khan, Shengli Xie, Qibin Zhao
Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations. CoRR (2018)
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. ISITA (2018)
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR (2018)
Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
TD-Regularized Actor-Critic Methods. CoRR (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. CoRR (2018)
Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal
Vprop: Variational Inference using RMSprop. CoRR (2017)
Katarzyna Olejnik, Italo Dacosta, Joana Soares Machado, Kévin Huguenin, Mohammad Emtiyaz Khan, Jean-Pierre Hubaux
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices. IEEE Symposium on Security and Privacy (2017)
Mohammad Emtiyaz Khan, Wu Lin
Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. CoRR (2017)
Mohammad Emtiyaz Khan, Wu Lin
Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. AISTATS (2017)
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen
Variational Adaptive-Newton Method for Explorative Learning. CoRR (2017)
Vincent Etter, Mohammad Emtiyaz Khan, Matthias Grossglauser, Patrick Thiran
Online Collaborative Prediction of Regional Vote Results. DSAA (2016)
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI (2016)
Mattia Carpin, Stefano Rosati, Mohammad Emtiyaz Khan, Bixio Rimoldi
UAVs using Bayesian Optimization to Locate WiFi Devices. CoRR (2015)
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR (2015)
Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger
Scalable Collaborative Bayesian Preference Learning. AISTATS (2014)
Young-Jun Ko, Mohammad Emtiyaz Khan
Variational Gaussian Inference for Bilinear Models of Count Data. ACML (2014)
Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML (3) (2013)
Mohammad Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression. NIPS (2012)
Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M. Marlin, Kevin P. Murphy
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. AISTATS (2012)
Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. ICML (2011)
Mohammad Emtiyaz Khan, Benjamin M. Marlin, Guillaume Bouchard, Kevin P. Murphy
Variational bounds for mixed-data factor analysis. NIPS (2010)
Baback Moghaddam, Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models. NIPS (2009)
Mohammad Emtiyaz Khan, Deshpande Narayan Dutt
An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG. IEEE Trans. Biomed. Eng. 54 (7) (2007)
Mohammad Emtiyaz Khan, D. Narayana Dutt
Expectation-maximization (EM) algorithm for instantaneous frequency estimation with Kalman smoother. EUSIPCO (2004)