Publications

• Federated ADMM from Bayesian Duality,
  (ICLR 2026) T. Möllenhoff^*, S. Swaroop^*, F. Doshi-Velez, M. E. Khan. [OpenReview]
• Information Geometry of Variational Bayes,
  (Information Geometry) M.E. Khan [ ArXiv ]
  
• Variational Learning Finds Flatter Solutions at the Edge of Stability,
  (NeurIPS 2025) A. Ghosh, B. Cong, R. Yokota, S. Ravishankar, R. Wang, M. Tao, M.E. Khan, T. Möllenhoff [ ArXiv ] Accepted as Spotlight
  
• U-ensembles: Improved diversity in the small data regime using unlabeled data,
  (AABI 2025) K. Pitas, H.A. Bourrous, J. Arbel
  
• Gaussian Pre-Activations in Neural Networks: Myth or Reality?,
  (TMLR 2025) P. Wolinski, J. Arbel [ ArXiv ]
  
• Revisiting concentration results for approximate Bayesian computation,
  (Bayesian Analysis 2025) H.D. Nguyen, T.T. Nguyen, J. Arbel, F. Forbes [ HAL ]
  
• Compact Memory for Continual Logistic Regression,
  (NeurIPS 2025) Y. Jung, H. Lee, W. Chen, T. Möllenhoff, Y. Li, J. Lee, M.E. Khan
• 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 ]
  
• Connecting Federated ADMM to Bayes,
  (ICLR 2025) S. Swaroop, M.E. Khan, F. Doshi-Velez [ OpenReview ] [ ArXiv ]
  
• Uncertainty-Aware Decoding with Minimum Bayes' Risk,
  (ICLR 2025) N. Daheim, C. Meister, T. Möllenhoff, I. Gurevych [ OpenReview ] [ ArXiv ]
  
• Variational Low-Rank Adaptation Using IVON,
  (Fine-Tuning in Modern ML (FITML) at NeurIPS 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
  
• Model Merging by Uncertainty-Based Gradient Matching,
  N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M.E. Khan [ ArXiv ]
  
• 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 Memory Perturbation Equation: Understanding Model’s Sensitivity to Data,
  (NeurIPS 2023) P. Nickl, L. Xu, D. Tailor, T. Möllenhoff, M.E. Khan [ arXiv ]
  
• The fine print on tempered posteriors,
  (ACML 2023) K. Pitas, J. Arbel [ 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 ]
• 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),
  
• The Bayesian Learning Rule,
  (JMLR) M.E. Khan, H. Rue [ JMLR 2023 ] [ arXiv ] [ Tweet ]
  
• Knowledge-Adaptation Priors,
  (NeurIPS 2021) M.E. Khan^*, Siddharth Swaroop^* [ arXiv ] [ Slides ] [ Tweet ] [ SlidesLive Video ]
• Dual Parameterization of Sparse Variational Gaussian Processes,
  (NeurIPS 2021) P. Chang, V. ADAM, M.E. Khan, A. Solin [ arXiv ]
• Continual Deep Learning by Functional Regularisation of Memorable Past,
  (NeurIPS 2020, Oral) P. Pan^*, S. Swaroop^*, A. Immer, R. Eschenhagen, R. E. Turner, M.E. Khan [ arXiv ] [ Code ] [ Poster ]
  
• Approximate Inference Turns Deep Networks into Gaussian Processes,
  (NeurIPS 2019) M.E. Khan, A. Immer, E. Abedi, M. Korzepa. [ arXiv ] [ Code ]
• Decoupled Variational Gaussian Inference,
  (NIPS 2014) M.E. Khan [ Paper and appendix ]
• Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models,
  (ICML 2013) M.E. Khan, A. Aravkin, M. Friedlander, M. Seeger [ Paper ]

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