List of Publications

For a list of code releases, see our research page.

Early Drafts/Preprints

  • Optimistic Estimation of Convergence in Markov Chains with the Average-Mixing Time,
    (Preprint) G. Wolfer, P. Alquier [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]
  • 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


  • 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]
  • Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI,
    (ICML 2024) T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, J.M.H. Lobato, A. Hubin, A. Immer, T. Karaletsos, M.E. Khan, A. Kristiadi, Y. Li, S. Mandt, C. Nemeth, M.A. Osborne, T.G.J. Rudner, D. Rügamer, Y.W.T., M. Welling, A.G. Wilson, R.uqi Zhang [arXiv]
  • Improved Estimation of Relaxation Time in Non-reversible Markov Chains,
    (Annals of Applied Probability) G. Wolfer, A. Kontorovich [Published version] [arXiv]
  • Model Merging by Uncertainty-Based Gradient Matching,
    (ICLR 2024) N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M.E. Khan [arXiv] [Code]
  • Conformal Prediction via Regression-as-Classification,
    (ICLR 2024) E. K. Guha, S. Natarajan, T. Möllenhoff, M.E. Khan, E. Ndiaye [OpenReview] [ArXiv] [Code]



  • 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]