List of Publications

Early Drafts/Preprints

  • Model Merging by Uncertainty-Based Gradient Matching,
    N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M.E. Khan [ ArXiv ]
  • Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution,
    (Preprint) S. Nigikata, P. Alquier, M. Imaizuimi [arXiv]
  • Variance-Aware Estimation of Kernel Mean Embedding,
    (Preprint) G. Wolfer, P. Alquier [arXiv]
  • Geometric Aspects of Data-Processing of Markov Chains,
    (Preprint) G. Wolfer, S. Watanabe [arXiv]
  • Optimal Quasi-Bayesian Reduced Rank Regression with Incomplete Response,
    (Preprint) T. T. Mai, P. Alquier [arXiv]
  • Concentration and Robustness of Discrepancy-based ABC via Rademacher Complexity,
    (Preprint) S. Legramanti, D. Durante, P. Alquier [arXiv]

In press / to appear


  • 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 [ Coming soon ]
  • The Bayesian Learning Rule,
    (JMLR) M.E. Khan, H. Rue [ 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 [ coming soon ]
  • 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]
  • Estimation of Copulas via Maximum Mean Discrepancy,
    (JASA) P. Alquier, B.-E. Chérief-Abdellatif, A. Derumigny, J.-D. Fermanian [Journal version] [arXiv]
  • Empirical and Instance-Dependent Estimation of Markov Chain and Mixing Time,
    (Scandinavian Journal of Statistics) G. Wolfer [arXiv] [Journal version]
  • Systematic Approaches to Generate Reversiblizations of Markov Chains,
    (IEEE Transactions on Information Theory) M. C.H. Choi, G. Wolfer [arXiv] [Early Access]
  • Learning and Identity Testing of Markov Chains,
    (Handbook of Statistics, Volume 49) G. Wolfer, A. Kontorovich [Journal version]
  • Exploiting Inferential Structure in Neural Processes,
    (UAI 2023) D. Tailor, M.E. Khan, E. Nalisnick [Published version] [arXiv] [Poster]
  • Information Geometry of Markov Kernels: a Survey,
    in "Advances in Information Geometry: Beyond the Conventional Approach",
    (Front. Phys. Sec. Statistical and Computational Physics) G. Wolfer, S. Watanabe [Journal version]
  • MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages,
    (ACL 2023) C. M. Bamba, D. Adelani, P. Nabende, J. O. Alabi, T. Sindane, H. Buzaaba,... [arXiv]
  • Geometric Reduction for Identity Testing of Reversible Markov Chains,
    (GSI 2023) G. Wolfer, S. Watanabe [Published version] [arXiv]
    Oral presentation.
  • Simplifying Momentum-based Riemannian Submanifold Optimization,
    (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</span>
  • Dimension-Free Empirical Entropy Estimation,
    (IEEE Transactions on Information Theory) D. Cohen, A. Kontorovich, A. Koolyk, G. Wolfer [Journal version] [arXiv]
  • The 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]
    Notable top-5% of all accepted papers.


  • 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 ]
  • MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition,
    (EMNLP 2022) D. Adelani, G. Neubig, S. Ruder, S. Rijhwani, M. Beukman, C. Palen-Michel, C. Lignos, J. Alabi, S. Muhammad, P. Nabende, B. Dione, A. Bukula, R. Mabuya, B. Dossou, B. Sibanda, H. Buzaaba, ..... [arXiv]
  • 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 [ OpenReview ] [Video] [Poster]
  • Deviation Inequalities for Stochastic Approximation by Averaging,
    (SPA) X. Fan, P. Alquier, P. Doukhan [Published version] [arXiv]
  • Understanding the Population Structure Correction Regression,
    (ICSTA 2022) T. T. Mai, P. Alquier [Published version] [arXiv]
  • Approximate Bayesian Inference: Reprint of the Special Issue Published in Entropy,
    (MDPI Books) P. Alquier (Editor) [Book page]
  • Tight Risk Bound for High Dimensional Time Series Completion,
    (EJS) P. Alquier, N. Marie, A. Rosier [Published version] [arXiv]
  • Finite Sample Properties of Parametric MMD Estimation: Robustness to Misspecification and Dependence,
    (Bernoulli) B.E. Chérief-Abdellatif, P. Alquier [Published version] [arXiv]