ジャーナル論文 / Journal
  1. Wolfer, G., and Kontorovich, A., "Improved estimation of relaxation time in nonreversible Markov chains", Ann. Appl. Probab. 34(1A), 249–276, (2024).
  2. Will, M. Wiesinger, Micke, Yildiz, Doriscoll, Kommu, Abbas, Arnt, Bauer, Erlewein, Fleck, Jäger, Latacz, Mooser, Schweizer, Umbrazunas, Wursten, Braum, Devlin, Ospelcaus, Qint, Soter, Walz, Smorra, and ulmer, "Image-Current Mediated Sympathetic Laser Cooling of a Single Proton in a Penning Trap Down to 170 mK Axial Temperature", Phys. Rev. Lett. 133,023002(133), 2–12, (2024).
  3. Wolfer, G., "Empirical and instance-dependent estimation of Markov chain and mixing time", Scandinavian Journal of Statistics (Early View), (2023).
  4. Piché, A., Thomas, V., Pardinas, R., Marino, J., Marconi, G., Pal, C., and Khan, M. E., "Bridging the Gap Between Target Networks and Functional Regularization", Transactions on Machine Learning Research, (2023).
  5. Khan, M., and Rue, H., "The Bayesian Learning Rule", J. Mach. Learn. Res. 1(46), (2023).
  6. Choi, M., and Wolfer, G., "Systematic Approaches to Generate Reversiblizations of Markov Chains", IEEE Transactions on Information Theory (Early Access), (2023).
  7. Alquier, P., Chérief-Abdellatif, B., Derumigny, A., and Jean-David, F., "Estimation of Copulas via Maximum Mean Discrepancy", Journal of the American Statistical Association 118(543), (2023).
  8. Abdulsamad, H., Nickl, P., Klink, P., and Peters, J., "Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics", IEEE Trans. Pattern Anal. Machine Intell. PP(99), 1–13, (2023).
  9. Fan, X., Alquier, P., and Doukhan, P., "Deviation Inequalities for Stochastic Approximation by Averaging", Stochastic Process. Appl. 152, 452–485, (2022).
  10. Chérief-Abdellatif, B., and Alquier, P., "Finite sample properties of parametric MMD estimation: Robustness to misspecification and dependence", Bernoulli 28(1), 181–213, (2022).
  11. Alquier, P., Marie, N., and Rosier, A., "Tight Risk Bound for High Dimensional Time Series Completion", Electron. J. Stat. 16(1), 3001–3035, (2022).
  12. Meunier, D., and Alquier, P., "Meta-Strategy for Learning Tuning Parameters with Guarantees", Entropy 23(10), (2021).
  13. Marconi, G., Camoriano, R., Rosasco, L., and Ciliberto, C., "Structured Prediction for CRISP Inverse Kinematics Learning With Misspecified Robot Models", IEEE Robot. Autom. Lett. 6(3), 5650–5657, (2021).
  14. Carel, L., and Alquier, P., "Simultaneous dimension reduction and clustering via the NMF-EM algorithm", Advances in Data Analysis and Classification 15(1), 231–260, (2021).
国際会議 / Proceedings
  1. Wolfer, G., and Watanabe, S., "Geometric Reduction for Identity Testing of Reversible Markov Chains", Lecture Notes in Computer Science (LNCS) International Conference on Geometric Science of Information (GSI), 2023 14071, 328–337, (2023).
  2. Tailor, D., Nalisnick, E., and Khan, M., "Exploiting Inferential Structure in Neural Processes", Uncertainty of Artificial Intelligence, 2089–2098, (2023).
  3. Nickl, P., Xu, L., Tailor, D., Moellenhoff, T., and Khan, E. M., "The Memory Perturbation Equation: Understanding Model’s Sensitivity to Data", Advances in Neural Information Processing Systems, NeurIPS 2023 36, 26923–26949, (2023).
  4. Lin, W., Duruisseaux, V., Leok, M., Nielsen, F., Khan, E. M., and Schmidt, M., "Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning", International Conference on Machine Learning 40, (2023).
  5. Erik, D., Siddharth, S., Kazuki, O., Rio, Y., Richard, T., Jose, H. M., and Khan, M., "Improving Continual Learning by Accurate Gradient Reconstructions of the Past", Transactions on Machine Learning Research (TMLR), (2023).
  6. Chang, P. E., Verma, P., John, S., Solin, A., and Khan, M., "Memory-Based Dual Gaussian Processes for Sequential Learning", International Conference on Machine Learning, (2023).
  7. Buzaaba, H., and Cheikh, D. M., "MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages", Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), (2023).
  8. Mai, T. T., and Alquier, P., "Understanding the Population Structure Correction Regression", Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA'22), (2022).
  9. Lin, W., Nielson, F., Khan, E., and Schmidt, M., "Tractable structured natural-gradient descent using local parameterizations", Proceedings of the 38 th International Conference on Machine Learning, Proceedings of Machine Learning Research, 6680–6691, (2021).
  10. Khan, E., and Swaroop, S., "Knowledge-Adaptation Priors", Advances in Neural Information Processing Systems 34, 19757–19770, (2021).
  11. Jain, A., Srijith, P., and Khan, E., "Subset-of-data variational inference for deep Gaussian-processes regression", Proceedings of the 37h Conference on Uncertainty in Artificial Intelligence, Proceedings of Machine Learning Research 161, 1362–1370, (2021).
  12. Immer, A., Bauer, M., Fortuin, V., Rätsch, G., and Khan, E., "Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning", Proceedings of the 38 th International Conference on Machine Learning, Proceedings of Machine Learning Research 139, 4563–4573, (2021).
  13. Doan, T., Bennani, M., Mazoure, B., Rabusseau, G., and Alquier, P., "A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix", Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research 130, 1072–1080, (2021).
  14. Alquier, P., "Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions.", Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research 139, 207–218, (2021).
  15. Adam, V., Paul, C., Khan, E., and Solin, A., "Dual Parameterization of Sparse Variational Gaussian Processes", Advances in Neural Information Processing Systems 34, 11474–11486, (2021).
レビュー / Review
  1. Wolfer, G., and Watanabe, S., "Information geometry of Markov Kernels: a survey", Front. Phys., Sec. Statistical and Computational Physics, (2023).
その他 / Other
  1. Khan, M., "Variational Bayes Made Easy", 5th Symposium on Advances in Approximate Bayesian Inference, (2023).