Expectation-maximization (EM) algorithm for instantaneous frequency estimation with Kalman smoother. - Mohammad Emtiyaz Khan; D. Narayana Dutt (EUSIPCO, 2004)

An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG. - Mohammad Emtiyaz Khan; Deshpande Narayan Dutt (IEEE Trans. Biomed. Eng., 2007)

Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models. - Baback Moghaddam; Benjamin M. Marlin; Mohammad Emtiyaz Khan; Kevin P. Murphy (NIPS, 2009)

Variational bounds for mixed-data factor analysis. - Mohammad Emtiyaz Khan; Benjamin M. Marlin; Guillaume Bouchard; Kevin P. Murphy (NIPS, 2010)

Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. - Benjamin M. Marlin; Mohammad Emtiyaz Khan; Kevin P. Murphy (ICML, 2011)

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression. - Mohammad Emtiyaz Khan; Shakir Mohamed; Kevin P. Murphy (NIPS, 2012)

A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. - Mohammad Emtiyaz Khan; Shakir Mohamed; Benjamin M. Marlin; Kevin P. Murphy (AISTATS, 2012)

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. - Mohammad Emtiyaz Khan; Aleksandr Y. Aravkin; Michael P. Friedlander; Matthias W. Seeger (ICML (3), 2013)

Variational Gaussian Inference for Bilinear Models of Count Data. - Young-Jun Ko; Mohammad Emtiyaz Khan (ACML, 2014)

Scalable Collaborative Bayesian Preference Learning. - Mohammad Emtiyaz Khan; Young-Jun Ko; Matthias W. Seeger (AISTATS, 2014)

UAVs using Bayesian Optimization to Locate WiFi Devices. - Mattia Carpin; Stefano Rosati; Mohammad Emtiyaz Khan; Bixio Rimoldi (CoRR, 2015)

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (CoRR, 2015)

Online Collaborative Prediction of Regional Vote Results. - Vincent Etter; Mohammad Emtiyaz Khan; Matthias Grossglauser; Patrick Thiran (DSAA, 2016)

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (UAI, 2016)

Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models. - Mohammad Emtiyaz Khan; Wu Lin (AISTATS, 2017)

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices. - Katarzyna Olejnik; Italo Dacosta; Joana Soares Machado; Kévin Huguenin; Mohammad Emtiyaz Khan; Jean-Pierre Hubaux (IEEE Symposium on Security and Privacy, 2017)

Variational Adaptive-Newton Method for Explorative Learning. - Mohammad Emtiyaz Khan; Wu Lin; Voot Tangkaratt; Zuozhu Liu; Didrik Nielsen (CoRR, 2017)

Vprop: Variational Inference using RMSprop. - Mohammad Emtiyaz Khan; Zuozhu Liu; Voot Tangkaratt; Yarin Gal (CoRR, 2017)

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. - Hongyi Ding; Mohammad Emtiyaz Khan; Issei Sato; Masashi Sugiyama (AISTATS, 2018)

Variational Message Passing with Structured Inference Networks. - Wu Lin; Nicolas Hubacher; Mohammad Emtiyaz Khan (ICLR (Poster), 2018)

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. - Mohammad Emtiyaz Khan; Didrik Nielsen; Voot Tangkaratt; Wu Lin; Yarin Gal; Akash Srivastava (ICML, 2018)

Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. - Mohammad Emtiyaz Khan; Didrik Nielsen (ISITA, 2018)

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. - Aaron Mishkin; Frederik Kunstner; Didrik Nielsen; Mark Schmidt 001; Mohammad Emtiyaz Khan (NeurIPS, 2018)

Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations. - Chao Li 013; Mohammad Emtiyaz Khan; Shengli Xie; Qibin Zhao (CoRR, 2018)

A Generalization Bound for Online Variational Inference. - Badr-Eddine Chérief-Abdellatif; Pierre Alquier; Mohammad Emtiyaz Khan (ACML, 2019)

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. - Wu Lin; Mohammad Emtiyaz Khan; Mark Schmidt 001 (ICML, 2019)

Scalable Training of Inference Networks for Gaussian-Process Models. - Jiaxin Shi; Mohammad Emtiyaz Khan; Jun Zhu 001 (ICML, 2019)

Approximate Inference Turns Deep Networks into Gaussian Processes. - Mohammad Emtiyaz Khan; Alexander Immer; Ehsan Abedi; Maciej Korzepa (NeurIPS, 2019)

Practical Deep Learning with Bayesian Principles. - Kazuki Osawa; Siddharth Swaroop; Mohammad Emtiyaz Khan; Anirudh Jain; Runa Eschenhagen; Richard E. Turner; Rio Yokota (NeurIPS, 2019)

VILD: Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (CoRR, 2019)

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures. - Wu Lin; Mohammad Emtiyaz Khan; Mark Schmidt 001 (CoRR, 2019)

AI for the Social Good (Dagstuhl Seminar 19082). - Claudia Clopath; Ruben De Winne; Mohammad Emtiyaz Khan; Tom Schaul (Dagstuhl Reports, 2019)

TD-regularized actor-critic methods. - Simone Parisi; Voot Tangkaratt; Jan Peters 001; Mohammad Emtiyaz Khan (Mach. Learn., 2019)

Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. - Chao Li 013; Mohammad Emtiyaz Khan; Zhun Sun; Gang Niu 001; Bo Han 003; Shengli Xie; Qibin Zhao (AAAI, 2020)

Handling the Positive-Definite Constraint in the Bayesian Learning Rule. - Wu Lin; Mark Schmidt 001; Mohammad Emtiyaz Khan (ICML, 2020)

Training Binary Neural Networks using the Bayesian Learning Rule. - Xiangming Meng; Roman Bachmann 001; Mohammad Emtiyaz Khan (ICML, 2020)

Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (ICML, 2020)

Fast Variational Learning in State-Space Gaussian Process Models. - Paul E. Chang; William J. Wilkinson; Mohammad Emtiyaz Khan; Arno Solin (MLSP, 2020)

Continual Deep Learning by Functional Regularisation of Memorable Past. - Pingbo Pan; Siddharth Swaroop; Alexander Immer; Runa Eschenhagen; Richard E. Turner; Mohammad Emtiyaz Khan (NeurIPS, 2020)

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. - Alexander Immer; Matthias Bauer 001; Vincent Fortuin; Gunnar Rätsch; Mohammad Emtiyaz Khan (ICML, 2021)

Tractable structured natural-gradient descent using local parameterizations. - Wu Lin; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (ICML, 2021)

Dual Parameterization of Sparse Variational Gaussian Processes. - Vincent Adam; Paul E. Chang; Mohammad Emtiyaz Khan; Arno Solin (NeurIPS, 2021)

Knowledge-Adaptation Priors. - Mohammad Emtiyaz Khan; Siddharth Swaroop (NeurIPS, 2021)

Subset-of-data variational inference for deep Gaussian-processes regression. - Ayush Jain; P. K. Srijith; Mohammad Emtiyaz Khan (UAI, 2021)

Beyond Target Networks: Improving Deep Q-learning with Functional Regularization. - Alexandre Piché; Joseph Marino; Gian Maria Marconi; Christopher Joseph Pal; Mohammad Emtiyaz Khan (CoRR, 2021)

Structured second-order methods via natural gradient descent. - Wu Lin; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (CoRR, 2021)

Can Calibration Improve Sample Prioritization? - Ganesh Tata; Gautham Krishna Gudur; Gopinath Chennupati; Mohammad Emtiyaz Khan (CoRR, 2022)

The Lie-Group Bayesian Learning Rule. - Eren Mehmet Kiral; Thomas Möllenhoff; Mohammad Emtiyaz Khan (AISTATS, 2023)

SAM as an Optimal Relaxation of Bayes. - Thomas Möllenhoff; Mohammad Emtiyaz Khan (ICLR, 2023)

Memory-Based Dual Gaussian Processes for Sequential Learning. - Paul Edmund Chang; Prakhar Verma; S. T. John; Arno Solin; Mohammad Emtiyaz Khan (ICML, 2023)

Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. - Wu Lin; Valentin Duruisseaux; Melvin Leok; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (ICML, 2023)

The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data. - Peter Nickl; Lu Xu; Dharmesh Tailor; Thomas Möllenhoff; Mohammad Emtiyaz Khan (NeurIPS, 2023)

Exploiting Inferential Structure in Neural Processes. - Dharmesh Tailor; Mohammad Emtiyaz Khan; Eric T. Nalisnick (UAI, 2023)

Simplifying Momentum-based Riemannian Submanifold Optimization. - Wu Lin; Valentin Duruisseaux; Melvin Leok; Frank Nielsen; Mohammad Emtiyaz Khan; Mark Schmidt 001 (CoRR, 2023)

Variational Bayes Made Easy. - Mohammad Emtiyaz Khan (CoRR, 2023)

The Bayesian Learning Rule. - Mohammad Emtiyaz Khan; Håvard Rue (J. Mach. Learn. Res., 2023)

Improving Continual Learning by Accurate Gradient Reconstructions of the Past. - Erik A. Daxberger; Siddharth Swaroop; Kazuki Osawa; Rio Yokota; Richard E. Turner; José Miguel Hernández-Lobato; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

Bridging the Gap Between Target Networks and Functional Regularization. - Alexandre Piché; Valentin Thomas; Joseph Marino; Rafael Pardinas; Gian Maria Marconi; Christopher Pal; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

Model Merging by Uncertainty-Based Gradient Matching. - Nico Daheim; Thomas Möllenhoff; Edoardo M. Ponti; Iryna Gurevych; Mohammad Emtiyaz Khan (ICLR, 2024)

Conformal Prediction via Regression-as-Classification. - Etash Kumar Guha; Shlok Natarajan; Thomas Möllenhoff; Mohammad Emtiyaz Khan; Eugène Ndiaye (ICLR, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - 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; et al. (ICML, 2024)

Variational Learning is Effective for Large Deep Networks. - Yuesong Shen; Nico Daheim; Bai Cong; Peter Nickl; Gian Maria Marconi; Clement Bazan; Rio Yokota; Iryna Gurevych; Daniel Cremers; Mohammad Emtiyaz Khan; Thomas Möllenhoff (ICML, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - 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; et al. (CoRR, 2024)

Variational Low-Rank Adaptation Using IVON. - Bai Cong; Nico Daheim; Yuesong Shen; Daniel Cremers; Rio Yokota; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2024)

How to Weight Multitask Finetuning? Fast Previews via Bayesian Model-Merging. - Hugo Monzón Maldonado; Thomas Möllenhoff; Nico Daheim; Iryna Gurevych; Mohammad Emtiyaz Khan (CoRR, 2024)

AI for Social Good (Dagstuhl Seminar 24082). - Claudia Clopath; Ruben De Winne; Mohammad Emtiyaz Khan; Jacopo Margutti (Dagstuhl Reports, 2024)

Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461). - Vincent Fortuin; Mohammad Emtiyaz Khan; Mark van der Wilk; Zoubin Ghahramani; Katharine Fisher (Dagstuhl Reports, 2024)

Connecting Federated ADMM to Bayes. - Siddharth Swaroop; Mohammad Emtiyaz Khan; Finale Doshi-Velez (ICLR, 2025)

Optimization Guarantees for Square-Root Natural-Gradient Variational Inference. - Navish Kumar; Thomas Möllenhoff; Mohammad Emtiyaz Khan; Aurélien Lucchi (Trans. Mach. Learn. Res., 2025)

Natural Variational Annealing for Multimodal Optimization. - Tâm Le Minh; Julyan Arbel; Thomas Möllenhoff; Mohammad Emtiyaz Khan; Florence Forbes (CoRR, 2025)

Variational Learning Induces Adaptive Label Smoothing. - Sin-Han Yang; Zhedong Liu; Gian Maria Marconi; Mohammad Emtiyaz Khan (CoRR, 2025)

Variational Visual Question Answering. - Tobias Jan Wieczorek; Nathalie Daun; Mohammad Emtiyaz Khan; Marcus Rohrbach (CoRR, 2025)

Variational Learning Finds Flatter Solutions at the Edge of Stability. - Avrajit Ghosh; Bai Cong; Rio Yokota; Saiprasad Ravishankar; Rongrong Wang; Molei Tao; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)

Federated ADMM from Bayesian Duality. - Thomas Möllenhoff; Siddharth Swaroop; Finale Doshi-Velez; Mohammad Emtiyaz Khan (CoRR, 2025)

Knowledge Adaptation as Posterior Correction. - Mohammad Emtiyaz Khan (CoRR, 2025)

Improving LoRA with Variational Learning. - Bai Cong; Nico Daheim; Yuesong Shen; Rio Yokota; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)

Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks. - Keigo Nishida; Eren Mehmet Kiral; Kenichi Bannai; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)