Khan, Mohammad Emtiyaz

50 publications

NeurIPS 2025 Compact Memory for Continual Logistic Regression Yohan Jung, Hyungi Lee, Wenlong Chen, Thomas Möllenhoff, Yingzhen Li, Juho Lee, Mohammad Emtiyaz Khan

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

TMLR 2025 Optimization Guarantees for Square-Root Natural-Gradient Variational Inference Navish Kumar, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Aurelien Lucchi

NeurIPS 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

ICLR 2024 Conformal Prediction via Regression-as-Classification Etash Kumar Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugene Ndiaye

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

ICML 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 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

ICML 2024 Variational Learning Is Effective for Large Deep Networks Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Bazan Clement Emile Marcel Raoul, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff

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

TMLR 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

NeurIPSW 2023 Conformal Prediction via Regression-as-Classification Etash Guha, Shlok Natarajan, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Eugene Ndiaye

UAI 2023 Exploiting Inferential Structure in Neural Processes Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick

TMLR 2023 Improving Continual Learning by Accurate Gradient Reconstructions of the past Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

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

NeurIPSW 2023 Model Merging by Gradient Matching Nico Daheim, Thomas Möllenhoff, Edoardo Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan

ICLR 2023 SAM as an Optimal Relaxation of Bayes Thomas Möllenhoff, 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

JMLR 2023 The Bayesian Learning Rule Mohammad Emtiyaz Khan, Håvard Rue

AISTATS 2023 The Lie-Group Bayesian Learning Rule Eren Mehmet Kiral, Thomas Moellenhoff, Mohammad Emtiyaz Khan

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

NeurIPSW 2022 Can Calibration Improve Sample Prioritization? Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan

NeurIPSW 2022 Practical Structured Riemannian Optimization with Momentum by Using Generalized Normal Coordinates Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt

NeurIPSW 2021 Beyond Target Networks: Improving Deep $q$-Learning with Functional Regularization Alexandre Piché, Joseph Marino, Gian Maria Marconi, Valentin Thomas, Christopher Pal, Mohammad Emtiyaz Khan

NeurIPS 2021 Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam, Paul Chang, Mohammad Emtiyaz Khan, Arno Solin

NeurIPS 2021 Knowledge-Adaptation Priors Mohammad Emtiyaz Khan, Siddharth Swaroop

UAI 2021 Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan

AAAI 2020 Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao

NeurIPS 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

ICMLW 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

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

ICML 2020 Training Binary Neural Networks Using the Bayesian Learning Rule Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan

ICML 2020 Variational Imitation Learning with Diverse-Quality Demonstrations Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama

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

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

ICML 2019 Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-Family Approximations Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt

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

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

MLJ 2019 TD-Regularized Actor-Critic Methods Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan

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

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

ICLR 2018 Variational Message Passing with Structured Inference Networks Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan

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

UAI 2016 Faster Stochastic Variational Inference Using Proximal-Gradient Methods with General Divergence Functions Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

NeurIPS 2015 Kullback-Leibler Proximal Variational Inference Mohammad Emtiyaz Khan, Pierre Baque, François Fleuret, Pascal Fua

NeurIPS 2014 Decoupled Variational Gaussian Inference Mohammad Emtiyaz Khan

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

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

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

NeurIPS 2010 Variational Bounds for Mixed-Data Factor Analysis Mohammad Emtiyaz Khan, Guillaume Bouchard, Kevin P. Murphy, Benjamin M. Marlin

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