publications-wombat

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Journal publications

  • J. Arbel, S. Girard, H. D. Nguyen, A. Usseglio-Carleve, Multivariate expectile-based
    distribution: properties, Bayesian inference, and applications, Journal of Statistical Planning and
    Inference, 2023.
  • T. Nguyen, F. Forbes, J. Arbel, H.D. Nguyen. Bayesian nonparametric mixture of experts for high-dimensional inverse problems, To appear in Journal of Nonparametric Statistics, (pdf)
  • Mark Chiu Chong, Hien Duy Nguyen, TrungTin Nguyen (2024). Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback–Leibler Divergence. Transactions on Machine Learning Research, link.

Conference publications

  • TrungTin Nguyen, Dung Ngoc Nguyen, Hien Duy Nguyen, Faicel Chamroukhi (2023). A non-asymptotic risk bound for model selection in high-dimensional mixture of experts via joint rank and variable selection. AJCAI 2023, link.
  • Huy Nguyen, TrungTin Nguyen, Nhat Ho (2023). Demystifying Softmax Gating Function in Gaussian Mixture of Experts. Thirty-seventh Conference on Neural Information Processing Systems, link.
  • G. Oudoumanessah, C. Lartizien, M. Dojat, F. Forbes. Towards frugal unsupervised detection of subtle abnormalities in medical imaging, International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, Vancouver, Canada, October 2023, (pdf)
  • J. Iollo, C. Heinkele, P. Alliez, F. Forbes. Gradient-based approach for sequential Bayesian optimal design, Colloque Francophone du Traitement du Signal et des Images, GRETSI, Grenoble, August 2023, (pdf)
  • S.-K.A. Ng, R. Tawiah, H. Nguyen, F. Forbes. Mixture of linear mixed models for clustering weighted random graphs. 25th international conference on computational statistiscs, Compstat 2023, London UK, August, 2023. Link
  • Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, Nhat Ho (2023). A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts. ICML 2024, link.
  • Huy Nguyen, TrungTin Nguyen, Khai Nguyen, Nhat Ho (2023). Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. AISTATS 2024, link.
  • J. Iollo, C. Heinkele, P. Alliez, F. Forbes. PASOA-PArticle baSed Bayesian Optimal Adaptive design, In Proceedings of The 41st International Conference on Machine Learning, ICML 2024. (pdf)
  • F. Forbes, H.D. Nguyen, T.T. Nguyen. Bayesian Likelihood Free Inference using Mixtures of Experts. In International Joint Conference on Neural Networks, IJCNN 2024. (pdf)

Working papers

  • H. D. Nguyen, T. T. Nguyen, J. Arbel, F. Forbes. Concentration results for approximate Bayesian computation without identifiability. Preprint and supplementary material (pdf)
  • J. Iollo, C. Heinkele, P. Alliez, F. Forbes. Bayesian Experimental Design via Contrastive Diffusions, (pdf)
  • H. Donancio, A. Barrier, L. F. South, F. Forbes. Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach (pdf)
  • G. Fort, F. Forbes, H.D. Nguyen. Sequential Sample Average Majorization Minimization, (pdf)