• Variational Learning Induces Adaptive Label Smoothing,
  S.H. Yang, Z. Liu, G.M. Marconi, M.E. Khan [ ArXiv ]
  
• Natural Variational Annealing for Multimodal Optimization,
  T. L. Minh, J. Arbel, T. Möllenhoff, M.E. Khan , F. Forbes [ ArXiv ]
  
• How to Weight Multitask Finetuning? Fast Previews via Bayesian Model-Merging,
  H. M. Maldonado, T. Möllenhoff, N. Daheim, I. Gurevych, M.E. Khan [ ArXiv ]
  

2025

• Optimization Guarantees for Square-Root Natural-Gradient Variational Inference,
  (TMLR 2025) N. Kumar, T. Moellenhoff, M.E. Khan, A. Lucchi [ OpenReview ]
  
• Connecting Federated ADMM to Bayes,
  (ICLR 2025) S. Swaroop, M.E. Khan, F. Doshi-Velez [ OpenReview ] [ ArXiv ]
  

2024

• Variational Low-Rank Adaptation Using IVON,
  (Fine-Tuning in Modern ML (FITML) at NuerIPS 2024) B. Cong, N. Daheim, Y. Shen, D. Cremers, R. Yokota, M.E. Khan, T. Möllenhoff [ OpenReview ] [ ArXiv ]
  
• Variational Learning is Effective for Large Deep Networks,
  (ICML 2024) Y. Shen*, N. Daheim*, B. Cong, P. Nickl, G.M. Marconi, C. Bazan, R. Yokota, I. Gurevych, D. Cremers, M.E. Khan, T. Möllenhoff
  [ ArXiv ] [ Blog ] [ Code ] Accepted as spotlight
  
• Position Paper: Bayesian Deep Learning is Needed in the Age of Large-Scale AI,
  (ICML 2024) With many authors [ ArXiv ]
• Model Merging by Uncertainty-Based Gradient Matching,
  (ICLR 2024) N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M.E. Khan [ ArXiv ] [ OpenReview ]
  
• Conformal Prediction via Regression-as-Classification,
  (ICLR 2024) E. K. Guha, S. Natarajan, T. Möllenhoff, M.E. Khan, E. Ndiaye [ OpenReview ] [ ArXiv ] [ Code ]
  

2023

• Improving Continual Learning by Accurate Gradient Reconstructions of the Past,
  (TMLR) E. Daxberger, S. Swaroop, K. Osawa, R. Yokota, R. turner, J. M. Hernández-Lobato, M.E. Khan [ OpenReview ]
  
• The Bayesian Learning Rule,
  (JMLR) M.E. Khan, H. Rue [ JMLR 2023 ] [ arXiv ] [ Tweet ]
  
• The Memory Perturbation Equation: Understanding Model’s Sensitivity to Data,
  (NeurIPS 2023) P. Nickl, L. Xu, D. Tailor, T. Möllenhoff, M.E. Khan [ arXiv ]
  
• Bridging the Gap Between Target Networks and Functional Regularization,
  (TMLR) A. Piché, V. Thomas, R. Pardinas, J. Marino, G. M. Marconi, C. Pal, M.E. Khan [ Openreview ]
• Variational Bayes Made Easy,
  (AABI 2023) M.E. Khan [arXiv]
  
• Exploiting Inferential Structure in Neural Processes,
  (UAI 2023) D. Tailor, M.E. Khan, E. Nalisnick [arXiv]
  
• Simplifying Momentum-based Riemannian Submanifold Optimization with Applications to Deep Learning,
  (ICML 2023) W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M.E. Khan, M. Schmidt [ ArXiv ]
  
• Memory-Based Dual Gaussian Processes for Sequential Learning,
  (ICML 2023) P. E. Chang, P. Verma, S. T. John, A. Solin, M.E. Khan [ ArXiv ] Accepted for an oral presentation
  
• Lie-Group Bayesian Learning Rule,
  (AISTATS 2023) E. M. Kiral T. Möllenhoff, M.E. Khan [ arXiv ]
  
• SAM as an Optimal Relaxation of Bayes,
  (ICLR 2023) T. Möllenhoff, M.E. Khan [ arXiv ] [ Tweet ]
  Accepted for an oral presentation, 5% of accepted papers (75 out of 5000 submissions),
  

2022

• Sequential Learning in GPs with Memory and Bayesian Leverage Score,
  (Continual Lifelong Workshop at ACML 2022) P. Verma, P. E. Chang, A. Solin, M.E. Khan [ OpenReview ]
  
• Practical Structured Riemannian Optimization with Momentum by using Generalized Normal Coordinates,
  (NeuReps Workshop at NeurIPS 2022) W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M.E. Khan, M. Schmidt [ OpenReview ]
  
• Can Calibration Improve Sample Prioritization?,
  (HITY Workshop at NeurIPS 2022) G. Tata, G. K. Gudur, G. Chennupati, M.E. Khan [ OpenReview ]
  
• Exploiting Inferential Structure in Neural Processes,
  (Workshop on Tractable Probabilistic Modeling at UAI, 2022 ) D. Tailor, M.E. Khan, E. Nalisnick [arXiv] [Video] [Poster]
  

2021

• Knowledge-Adaptation Priors,
  (NeurIPS 2021) M.E. Khan, S. Swaroop [ arXiv ] [ OpenReview ] [ Slides ] [ Tweet ] [ SlidesLive Video ]
• Dual Parameterization of Sparse Variational Gaussian Processes,
  (NeurIPS 2021) P. Chang, V. ADAM, M.E. Khan, A. Solin [ arXiv ] [ Code ]
• Subset-of-Data Variational Inference for Deep Gaussian-Process Regression,
  (UAI 2021) A. Jain, P.K. Srijith, M.E. Khan [ arXiv ] [ Code ]
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning,
  (ICML 2021) A. Immer, M. Bauer, V. Fortuin, G. Ratsch, M.E. Khan, [ arXiv ]
• Tractable structured natural gradient descent using local parameterizations,
  (ICML 2021) W. Lin, F. Nielsen, M.E. Khan, M. Schmidt [ arXiv ]
• Structured second-order methods via natural gradient descent,
  (ICML 2021 Workshop on "Beyond first-order methods in ML systems)
  W. Lin, F. Nielsen, M.E. Khan, M. Schmidt [ arXiv ]

2020

• Learning Algorithms from Bayesian Principles,
  (Draft) M.E. Khan, H. Rue [ Draft version ]
  [ Full version on arXiv ] under a new title "The Bayesian Learning Rule"
• Continual Deep Learning by Functional Regularisation of Memorable Past
  (NeurIPS 2020) P. Pan^*, S. Swaroop^*, A. Immer, R. Eschenhagen, R. E. Turner, M.E. Khan [ arXiv ] [ Code ] [ Poster ]
  Accepted for an oral presentation, 1% of all submisssions (105 out of 9454 submissions),
  
• Fast Variational Learning in State-Space Gaussian Process Models ,
  (MLSP 2020) P. E. Chang, W. J. Wilkinson, M.E. Khan, A. Solin [ arXiv ]
• AI for Social Good: Unlocking the Opportunity for Positive Impact,
  (Nature Communications 2020) with Nenad Tomašev and many others
  [ Paper ] [ Declaration on AI2SG ] [ Dagstuhl AI4SG 2019 ] [ Press Release ]
• Training Binary Neural Networks using the Bayesian Learning Rule,
  (ICML 2020) X. Meng, R. Bachmann, M.E. Khan [ arXiv ] [ Code ]
• Handling the Positive-Definite Constraint in the Bayesian Learning Rule,
  (ICML 2020) W. Lin, M. Schmidt, M.E. Khan [ arXiv ]
• VILD: Variational Imitation Learning with Diverse-quality Demonstrations,
  (ICML 2020) V. Tangkaratt, B. Han, M.E. Khan, M. Sugiyama [ arXiv ]
• Exact Recovery of Low-rank Tensor Decomposition under Reshuffling,
  (AAAI 2020) C. Li, M.E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, Q. Zhao [ arXiv ]

2019

• Practical Deep Learning with Bayesian Principles,
  (NeurIPS 2019) K. Osawa, S. Swaroop, A. Jain, R. Eschenhagen, R.E. Turner, R. Yokota, M.E. Khan. [ arXiv ] [ Code ]
• Approximate Inference Turns Deep Networks into Gaussian Processes,
  (NeurIPS 2019) M.E. Khan, A. Immer, E. Abedi, M. korzepa. [ arXiv ] [ Code ]
• A Generalization Bound for Online Variational Inference (best paper award),
  (ACML 2019) Badr-Eddine Chérief-Abdellatif, Pierre Alquier, M.E. Khan. [ arXiv ]
• Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations,
  (ICML, 2019) W. Lin, M.E. Khan, M. Schmidt. [ arXiv ] [ paper ] [ Appendix ] [ Code ]
  Also appeared at the Symposium on Advances in Approximate Bayesian Inference at NeurIPS 2018 [ Short Paper ]
  
• Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures,
  (ICML workshop on Stein's Method in ML and Stats, 2019) W. Lin, M.E. Khan, M. Schmidt. [ arXiv ]
• Scalable Training of Inference Networks for Gaussian-Process Models,
  (ICML, 2019) J. Shi, M.E. Khan, J. Zhu. [ arXiv ] [ paper ] [ Appendix ] [ Code ]
• TD-Regularized Actor-Critic Methods,
  (Machine Learning, 2019. A short version appeared at EWRL 2018)
  S. Parisi, V. Tangkaratt, J. Peters, M.E. Khan. [ Springer Online Version ] [ arXiv ] [ Short version at EWRL 2018 ]

2018

• SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient,
  (NeurIPS 2018) A. Miskin, F. Kunstner, D. Nielsen, M. Schmidt, M.E. Khan. [ arXiv ] [ Poster] [ 3-min Video] [ Code ]
• Natural Variational Continual Learning,
  (Continual Learning Workshop at NIPS 2018)
  H. Tseran, M.E. Khan , T. Harada, T. Bui [ Paper ].
• Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models,
  (ISITA 2018) M.E. Khan and D. Nielsen, [ arXiv ] [ IEEE explore ] [ Slides ]
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam,
  (ICML 2018) M.E. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, and A. Srivastava, [ arXiv Version ] [ Code ] [ Slides ]
• Variational Message Passing with Structured Inference Networks,
  (ICLR 2018) W. Lin, N. Hubacher, and M.E. Khan, [ Paper ] [ arXiv Version ] [ Code ]
• Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling,
  (AI-Stats 2018) H. Ding, M.E. Khan, I. sato, M. Sugiyama, [ Paper ] [ Appendix ] [ Code ]

2016

• Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions,
  (UAI 2016) M.E. Khan, R. Babanezhad, W. Lin, M. Schmidt, M. Sugiyama [ Paper + Appendix ] [ Code ]
• Online Collaborative Prediction of Regional Vote Results,
  (IEE Intenational Conference on Data Science and Advanced analytics (DSAA) 2016)
  V. Etter M.E. Khan, M. Grossglausser, and P. Thiran [ Paper ] [ Code & Data ]
• Variational Inference on Deep Exponential Family by using Variational Inferences on Conjugate Models,
  (Workshop on Bayesian Deep Learning at NIPS 2016)
  M.E. Khan and Wu Lin [ Paper ]

2015

• Kullback-Leibler Proximal Variational Inference,
  (NIPS 2015) M.E. Khan, P. Baque, F. Fleuret, P. Fua. [ Paper ] [ Appendix ]
• Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence,
  (NIPS 2015, Workshop on Advances in Approximate Bayesian Inference)
  M.E. Khan, R. Babanezhad, W. Lin, M. Schmidt, M. Sugiyama [ NIPS workshop version ] [ arXiv ]
• UAVs using Bayesian Optimization to Locate WiFi Devices,
  (NIPS 2015, BayesOpt Workshop) M. Carpin, S. Rosati, M.E. Khan, B. Rimoldi [ NIPS workshop version ] [ arXiv ]

2014

• Decoupled Variational Gaussian Inference,
  (NIPS 2014) M.E. Khan [ Paper and appendix ]
• Variational Gaussian Inference for Bilinear Models of Count Data,
  (ACML 2014) Y.J. Ko, M.E. Khan [ Paper ]
• Scalable collaborative Bayesian preference learning,
  (AI-Stats 2014, NIPS Workshop 2014) M.E. Khan ,Y.J. Ko, M. Seeger [ Paper ] [ Appendix ] [ Talk ]

2013

• Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models,
  (ICML 2013) M.E. Khan , A. Aravkin, M. Friedlander, M. Seeger [ Paper ]

• Variational Learning for Latent Gaussian Models of Discrete Data,
  (PhD thesis) M.E. Khan [ Link to pdf ]
• Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression,
  (NIPS 2012) M.E. Khan , S. Mohamed, K. Murphy [ pdf ] [ poster ] [ code ]
• Large-scale Approximate Bayesian Inference for Exponential Family Latent Gaussian Models,
  (ISBA 2012, poster) M.E. Khan, S. Mohamed, K. Murphy
• A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models,
  (AIstats 2012) M.E. Khan, S. Mohamed, B. Marlin, K. Murphy [ pdf ] [ poster ] [ code ] [ datasets]

2011

• Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models,
  (ICML 2011) B. Marlin, M. E. Khan, K. Murphy [ pdf ] [ presentation ] [ poster ] [ code ] [ appendix ]

2010

• Variational Bounds for Mixed-Data Factor Analysis,
  (NIPS 2010) M. E. Khan, B. Marlin, G. Bouchard, K. Murphy [ pdf ] [ poster ] [ MATLAB code ] [ corrected version ]
  Our implementation for mixture model had a bug, the corrected version contains new results.

2009

• Accelerating Bayesian Structural Inference for Non-decomposable Gaussian Graphical Model,
  (NIPS 2009, Oral) B. Mogaddham, B. Marlin, M. E. Khan, K. Murphy [ pdf ] [ poster ] [ MATLAB code (Ben’s website) ]

Before 2008

• An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG,
  (IEEE Transactions on Biomedical Engineering, 2007) M.E. Khan, D. N. Dutt [ pdf ] [ MATLAB code ]
• State Estimation with Wireless Devices,
  Third Int. Conf. Intelligent Sensing and Information Processing (ICISIP), 2005
  M.E. Khan, H. Raghavan, J. Brahmajosyula, S. K. Ramalingam, S. Narasimhan [ pdf ]
• Hybrid System Framework for State Estimation in Systems with Wireless Devices,
  Annual Meeting of American Institute of Chemical Engineers (AIChe), 2005
  M.E. Khan, H. Raghavan, J. Brahmajosyula, S.K. Ramalingam, S. Narasimhan
• Expectation-Maximization (EM) Algorithm for Instantaneous Frequency Estimation with Kalman Smoother (EUSIPCO 2004) M. E. Khan, D.N. Dutt [ pdf ]
• Estimation of ERS/ERD with Kalman Smoother: An EM Algorithm Approach,
  BIOSIGNAL 2004 M. E. Khan, D. N. Dutt