Preprints/ Publications (by journal/ conference)

(* = equal contribution )
(** = alphabetical order )
(† = co-last author )

Journal Submissions

• Partial differential equation barriers to identifiability in infinite mixture models . Under review.
  Dung Le, Nicola Bariletto, Alessandro Rinaldo, Nhat Ho.

• Convergence rates for latent mixing measures in infinite homoscedastic location-scale mixture models . Under review.
  Nicola Bariletto*, Dung Le*, Alessandro Rinaldo, Nhat Ho.

• On the geometry of separation in finite Gaussian mixtures . Under review.
  Huy Nguyen*, Dung Le*, Alessandro Rinaldo, Nhat Ho.

• Characterizing heterogeneous rates in finite mixture estimation via partial optimal transport . Under review.
  Dung Le*, Huy Nguyen*, Trang Pham, Alessandro Rinaldo, Nhat Ho.

• On expert estimation in hierarchical mixture of experts: Beyond softmax gating functions . Under review.
  Huy Nguyen*, Xing Han*, Carl Harris, Suchi Saria, Nhat Ho.

• On DeepSeekMoE: Statistical benefits of shared experts and normalized sigmoid gating . Under review.
  Huy Nguyen, Thong Doan, Quang Pham, Nghi Bui, Nhat Ho†, Alessandro Rinaldo†.

• On Bayesian Softmax-Gated Mixture-of-Experts Models . Under review.
  Nicola Bariletto*, Huy Nguyen*, Nhat Ho, Alessandro Rinaldo.

• Sigmoid self-attention is better than softmax self-attention: A mixture-of-experts perspective . Under review.
  Fanqi Yan*, Huy Nguyen*, Pedram Akbarian, Nhat Ho†, Alessandro Rinaldo†.

• Quadratic gating functions in mixture of experts: A statistical insight . Under review.
  Pedram Akbarian*, Huy Nguyen*, Xing Han*, Nhat Ho.

• Data-driven DRO and economic decision theory: An analytical synthesis and some Bayesian advancements . Under review.
  Nicola Bariletto, Khai Nguyen, Nhat Ho.

• Conformalized Bayesian inference, with applications to random partition models . Under review.
  Nicola Bariletto, Nhat Ho, Alessandro Rinaldo.

• A model-independent approach for federated learning in heterogeneous and label-scarce environments . Under review.
  Disha Makhija, Nhat Ho†, Joydeep Ghosh†.

• An exponentially increasing step-size for parameter estimation in statistical models. Under review.
  Nhat Ho**, Tongzheng Ren**, Purnamrita Sarkar**, Sujay Sanghavi**, Rachel Ward**.

• Probabilistic best subset selection via gradient-based optimization. Under review.
  Mingzhang Yin, Nhat Ho, Bowei Yan, Xiaoning Qian, Mingyuan Zhou.

• On mean-field variational inference: Non-asymptotic approximation, Bayesian inference, and parameter estimation. To be submitted.
  Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan.

Conference Submissions

• A statistical theory of gated attention through the lens of hierarchical mixture of experts . Under review.
  Nguyen Hoang Viet, Tuan Minh Pham, Thinh Cao Huy, Tan Dinh, Huy Nguyen, Nhat Ho†, Alessandro Rinaldo†.

• Rethinking multinomial logistic mixture of experts with sigmoid gating function . Under review.
  Tuan Minh Pham, Thinh Cao Huy, Nguyen Hoang Viet, Huy Nguyen, Nhat Ho†, Alessandro Rinaldo†.

• Improving minimax estimation rates for contaminated mixture of multinomial logistic experts via expert heterogeneity . Under review.
  Fanqi Yan, Le Quang Dung, Huyen Trang Pham, Huy Nguyen, Nhat Ho, Nhat Ho.

• Fast model selection and stable optimization for softmax-gated multinomial-logistic mixture of experts models . Under review.
  TrungKhang Tran, TrungTin Nguyen, Md Abul Bashar, Nhat Ho, Richi Nayak, Christopher Drovandi.

• Shape-adapting gated experts: Dynamic expert routing for colonoscopic lesion segmentation . Under review.
  Huy Thai*, Nguyen Vu*, Minh Phan, Thinh Ly, Tram Dinh, Truc Nguyen, Nhat Ho.

• Leveraging hierarchical taxonomies in prompt-based continual learning . Under review.
  Quyen Tran, Minh Le, Tuan Truong, Dinh Phung, Linh Ngo, Thien Nguyen, Nhat Ho, Trung Le.

• CompeteSMoE - Effective training of sparse mixture of experts via competition . Under review.
  Quang Pham, Truong Giang Do, Huy Nguyen, TrungTin Nguyen, Chenghao Liu, Mina Sartipi, Binh T. Nguyen, Savitha Ramasamy, Xiaoli Li, Steven Hoi†, Nhat Ho†.

Journal Publications

• [J.20] Convergence rates for softmax gating mixture of experts . IEEE Transactions on Information Theory, 2026.
  Huy Nguyen, Nhat Ho†, Alessandro Rinaldo†.

• [J.19] Instability, computational efficiency, and statistical accuracy . Journal of Machine Learning Research (JMLR), 2025.
  Nhat Ho*, Raaz Dwivedi*, Koulik Khamaru*, Martin J. Wainwright, Michael I. Jordan, Bin Yu.

• [J.18] MGPATH: A vision-language model with multi-granular prompt learning for few-shot whole slide pathology classification . Transactions on Machine Learning Research (TMLR), 2025.
  Anh-Tien Nguyen, Duy Minh Ho Nguyen, Nghiem Tuong Diep, Trung Quoc Nguyen, Nhat Ho, Jacqueline Michelle Metsch, Miriam Cindy Maurer, Daniel Sonntag, Hanibal Bohnenberger, and Anne-Christin Hauschild.

• [J.17] Global optimality of the EM algorithm for mixtures of two linear regression . IEEE Transactions on Information Theory, 2024.
  Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis, Nhat Ho.

• [J.16] On the computational and statistical complexity of over-parameterized matrix sensing . Journal of Machine Learning Research (JMLR), 2024.
  Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis.

• [J.15] On integral theorems and their statistical properties . Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2024.
  Nhat Ho**, Stephen G. Walker**.

• [J.14] Statistical and computational complexities of BFGS quasi-Newton method for generalized linear models. Transactions on Machine Learning Research (TLMR), 2024.
  Qiujiang Jin, Tongzheng Ren, Nhat Ho, Aryan Mokhtari.

• [J.13] A diffusion process perspective on the posterior contraction rates for parameters. SIAM Journal on Mathematics of Data Science (SIMODS), 2023.
  Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan.

• [J.12] A Bayesian perspective of convolutional neural networks through a deconvolutional generative model. Journal of Machine Learning Research (JMLR), 2023.
  Nhat Ho*, Tan Nguyen*, Ankit Patel, Anima Anandkumar, Michael I. Jordan, Richard Baraniuk.

• [J.11] Bayesian consistency with the supremum metric . Statistica Sinica, 2022.
  Nhat Ho**, Stephen G. Walker**.

• [J.10] Convergence rates for Gaussian mixtures of experts. Journal of Machine Learning Research (JMLR), 2022.
  Nhat Ho, Chiao-Yu Yang, Michael I. Jordan.

• [J.9] On the efficiency of entropic regularized algorithms for optimal transport . Journal of Machine Learning Research (JMLR), 2022.
  Tianyi Lin, Nhat Ho, Michael I. Jordan.

• [J.8] On the complexity of approximating multi-marginal optimal transport. Journal of Machine Learning Research (JMLR), 2022.
  Tianyi Lin*, Nhat Ho*, Marco Cuturi, Michael I. Jordan.

• [J.7] On posterior contraction of parameters and interpretability in Bayesian mixture modeling . Bernoulli 27 (4), 2159-2188, 2021.
  Aritra Guha, Nhat Ho, XuanLong Nguyen.

• [J.6] Singularity, misspecification, and the convergence rate of EM. Annals of Statistics, 48(6), 3161-3182, 2020.
  Raaz Dwivedi*, Nhat Ho*, Koulik Khamaru*, Martin J. Wainwright, Michael I. Jordan, Bin Yu.

• [J.5] On efficient multilevel clustering via Wasserstein distances. Journal of Machine Learning Research (JMLR), 22, 1-43, 2021.
  Viet Huynh*, Nhat Ho*, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Phung.

• [J.4] Robust estimation of mixing measures in finite mixture models. Bernoulli, 26(2), 828-857, 2020.
  Nhat Ho, XuanLong Nguyen, Ya'acov Ritov.

• [J.3] Singularity structures and impacts on parameter estimation behavior in finite mixtures of distributions. SIAM Journal on Mathematics of Data Science (SIMODS), 1(4), 730–758, 2019.
  Nhat Ho and XuanLong Nguyen.

• [J.2] Convergence rates of parameter estimation for some weakly identifiable finite mixtures. Annals of Statistics, 44(6), 2726-2755, 2016.
  Nhat Ho and XuanLong Nguyen.

• [J.1] On strong identifiability and convergence rates of parameter estimation in finite mixtures. Electronic Journal of Statistics, 10(1), 271-307, 2016.
  Nhat Ho and XuanLong Nguyen.

Conference Publications (Machine Learning, Computer Vision, Signal Processing, Time Series Analysis)

• [C.93] On the expressiveness of visual prompt experts. ICLR, 2026.
  Minh Le*, Anh Nguyen*, Huy Nguyen, Chau Nguyen, Anh Tran, Nhat Ho.

• [C.92] One-prompt strikes back: Sparse mixture of experts for prompt-based continual learning. ICLR, 2026.
  Minh Le, Bao-Ngoc Dao, Quyen Tran, Huy Nguyen, Anh Nguyen, Nhat Ho.

• [C.91] Fast estimation of Wasserstein distances via regression on sliced Wasserstein distances. ICLR, 2026.
  Khai Nguyen, Hai Nguyen, Nhat Ho.

• [C.90] FACET: A fragment-aware conformer ensemble Transformer. ICLR, 2026.
  Duy Nguyen, Trung Nguyen, Ha Le, Mai Truong, Tin Nguyen, Nhat Ho, Khoa Doan, Duy Duong-Tran, Li Shen, Daniel Sonntag, James Zou, Mathias Niepert, Hyojin Kim, Jonathan E Allen.

• [C.89] Dendrograms of mixing measures for softmax-gated Gaussian mixture of experts: Consistency without model sweeps . AISTATS, 2026 (Spotlight).
  Hai Do, Trung Nguyen Mai, TrungTin Nguyen, Nhat Ho, Binh T. Nguyen, Christopher Drovandi.

• [C.88] On barycenter computation: Semi-unbalanced optimal transport-based method on Gaussians. AISTATS, 2026.
  Hai Nguyen*, Dung Le*, Phi Nguyen, Tung Pham, Nhat Ho.

• [C.87] SAGE: Shape-adapting gated experts for adaptive histopathology image segmentation. CVPR, 2026 (Findings).
  Gia Huy Thai*, Hoang-Nguyen Vu*, Anh-Minh Phan, Quang-Thinh Ly, Tram Dinh, Thi-Ngoc-Truc Nguyen, Nhat Ho.

• [C.86] On minimax estimation of parameters in softmax-contaminated mixture of experts. Advances in NeurIPS, 2025.
  Fanqi Yan, Huy Nguyen, Dung Le, Pedram Akbarian, Nhat Ho†, Alessandro Rinaldo†.

• [C.85] Exgra-med: Extended context graph alignment for medical vision-language models. Advances in NeurIPS, 2025.
  Duy Nguyen, Nghiem Diep, Trung Nguyen, Hoang-Bao Le, Tai Nguyen, Anh-Tien Nguyen, Tin Nguyen, Nhat Ho, Pengtao Xie, Roger Wattenhofer, Daniel Sonntag, James Zou, Mathias Niepert.

• [C.84] Beyond losses reweighting: Empowering multi-task learning via the generalization perspective. International Conference on Computer Vision (ICCV), 2025 (Spotlight).
  Hoang Phan, Lam Tran, Quyen Tran, Ngoc Tran, Tuan Truong, Qi Lei, Nhat Ho, Dinh Phung, Trung Le.

• [C.83] RepLoRA: Reparameterizing low-rank adaptation via the perspective of mixture of experts. Proceedings of the ICML, 2025.
  Tuan Truong*, Chau Nguyen*, Huy Nguyen*, Minh Le, Trung Le, Nhat Ho.

• [C.82] On zero-initialized attention: Optimal prompt and gating factor estimation. Proceedings of the ICML, 2025.
  Nghiem Diep*, Huy Nguyen*, Chau Nguyen*, Minh Le, Duy Nguyen, Daniel Sonntag, Mathias Niepert,Nhat Ho.

• [C.81] Lightspeed geometric dataset distance via sliced optimal transport. Proceedings of the ICML, 2025.
  Khai Nguyen*, Hai Nguyen*, Tuan Pham, Nhat Ho.

• [C.80] Improving generalization with flat Hilbert Bayesian inference. Proceedings of the ICML, 2025.
  Tuan Truong*, Quyen Tran*, Quan Pham*, Dinh Phung, Nhat Ho, Trung Le.

• [C.79] Statistical advantages of perturbing cosine router in sparse mixture of experts . ICLR, 2025.
  Huy Nguyen, Pedram Akbarian*, Trang Pham*, Trang Nguyen*, Shujian Zhang Nhat Ho.

• [C.78] Revisiting prefix-tuning: Statistical benefits of reparameterization among prompts . ICLR, 2025.
  Minh Le*, Chau Nguyen*, Huy Nguyen*, Quyen Tran, Trung Le, Nhat Ho.

• [C.77] X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios . ICLR, 2025.
  Yichen Xie, Chenfeng Xu, Chensheng Peng, Shuqi Zhao, Nhat Ho, Alexander T. Pham, Mingyu Ding, Wei Zhan, Masayoshi Tomizuka.

• [C.76] Marginal fairness sliced Wasserstein barycenter . ICLR, 2025 (Spotlight).
  Khai Nguyen, Hai Nguyen, Nhat Ho.

• [C.75] Understanding expert structures on minimax parameter estimation in contaminated mixture of experts . AISTATS, 2025.
  Fanqi Yan*, Huy Nguyen*, Dung Le*, Pedram Akbarian, Nhat Ho.

• [C.74] Backdoor attack in prompt-based continual learning . AAAI, 2025.
  Trang Nguyen, Anh Tran, Nhat Ho.

• [C.73] Sigmoid gating is more sample efficient than softmax gating in mixture of experts . Advances in NeurIPS, 2024.
  Huy Nguyen, Nhat Ho†, Alessandro Rinaldo†.

• [C.72] Bayesian nonparametrics meets data-driven distributionally robust optimization . Advances in NeurIPS, 2024.
  Nicola Bariletto, Nhat Ho.

• [C.71] FuseMoE: Mixture-of-experts Transformers for fleximodal fusion . Advances in NeurIPS, 2024.
  Xing Han, Huy Nguyen, Carl William Harris, Nhat Ho†, Suchi Saria†.

• [C.70] Mixture of experts meets prompt-based continual learning . Advances in NeurIPS, 2024.
  Minh Le, An Nguyen*, Huy Nguyen*, Trang Nguyen*, Trang Pham*, Linh Van Ngo, Nhat Ho.

• [C.69] Hierarchical hybrid sliced Wasserstein: A scalable metric for heterogeneous joint distributions . Advances in NeurIPS, 2024.
  Khai Nguyen, Nhat Ho.

• [C.68] A Bayesian approach for personalized federated learning in heterogeneous settings. Advances in NeurIPS, 2024.
  Disha Makhija, Nhat Ho†, Joydeep Ghosh†.

• [C.67] Is temperature sample efficient for softmax Gaussian mixture of experts? . Proceedings of the ICML, 2024.
  Huy Nguyen, Pedram Akbarian, Nhat Ho.

• [C.66] On least square estimation in softmax gating mixture of experts . Proceedings of the ICML, 2024.
  Huy Nguyen, Nhat Ho†, Alessandro Rinaldo†.

• [C.65] Sliced Wasserstein with random-path projecting directions . Proceedings of the ICML, 2024.
  Khai Nguyen, Shujian Zhang, Tam Le, Nhat Ho.

• [C.64] Neural collapse for cross-entropy class-imbalanced learning with unconstrained ReLU feature model . Proceedings of the ICML, 2024.
  Hien Dang, Tho Tran, Tan Nguyen†, Nhat Ho†.

• [C.63] Structure-aware E(3)-invariant molecular conformer Aggregation Networks . Proceedings of the ICML, 2024.
  Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An Thai Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert.

• [C.62] A general theory for softmax gating multinomial logistic mixture of experts . Proceedings of the ICML, 2024.
  Huy Nguyen, Pedram Akbarian, Tin Nguyen, Nhat Ho.

• [C.61] Improving computational complexity in statistical models with second-order information . Proceedings of the ICML, 2024.
  Tongzheng Ren, Pedram Akbarian, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho.

• [C.60] Quasi-Monte Carlo for 3D sliced Wasserstein . ICLR, 2024 (Spotlight).
  Khai Nguyen, Nicola Bariletto, Nhat Ho.

• [C.59] Statistical perspective of top-K sparse softmax gating mixture of experts . ICLR, 2024.
  Huy Nguyen, Pedram Akbarian, Fanqi Yan, Nhat Ho.

• [C.58] Beyond vanilla variational autoencoders: Detecting posterior collapse in conditional and hierarchical variational autoencoders . ICLR, 2024.
  Hien Dang, Tho Tran, Tan Nguyen†, Nhat Ho†.

• [C.57] Sliced Wasserstein estimation with control variates . ICLR, 2024.
  Khai Nguyen, Nhat Ho.

• [C.56] Diffeomorphic mesh deformation via efficient optimal transport for cortical surface reconstruction. ICLR, 2024.
  Tung Le, Khai Nguyen, Shanlin Sun, Kun Han, Nhat Ho, Xiaohui Xie.

• [C.55] Revisiting deep audio-text retrieval through the lens of transportation. ICLR, 2024.
  Manh Luong, Khai Nguyen, Nhat Ho, Reza Haf, Dinh Phung, Lizhen Qu.

• [C.54] Towards convergence rates for parameter estimation in Gaussian-gated mixture of experts . AISTATS, 2024.
  Huy Nguyen, Tin Nguyen, Khai Nguyen, Nhat Ho.

• [C.53] On parameter estimation in deviated Gaussian mixture of experts. AISTATS, 2024.
  Huy Nguyen, Khai Nguyen, Nhat Ho.

• [C.52] Integrating efficient optimal transport and functional maps For unsupervised shape correspondence learning. Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  Thanh Tung Le, Khai Nguyen, shanlin sun, Nhat Ho, Xiaohui Xie.

• [C.51] Fast approximation of the generalized sliced-Wasserstein distance. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024.
  Dung Le*, Huy Nguyen*, Khai Nguyen*, Trang Nguyen*, Nhat Ho.

• [C.50] Demystifying softmax gating function in Gaussian mixture of experts . Advances in NeurIPS, 2023 (Spotlight).
  Huy Nguyen, Tin Nguyen, Nhat Ho.

• [C.49] Energy-based sliced Wasserstein distance . Advances in NeurIPS, 2023.
  Khai Nguyen, Nhat Ho.

• [C.48] Minimax optimal rate for parameter estimation in multivariate deviated models. Advances in NeurIPS, 2023.
  Dat Do*, Huy Nguyen*, Khai Nguyen, Nhat Ho.

• [C.47] Markovian sliced Wasserstein distances: Beyond independent projections . Advances in NeurIPS, 2023.
  Khai Nguyen, Tongzheng Ren, Nhat Ho.

• [C.46] Designing robust transformers using robust kernel density estimation. Advances in NeurIPS, 2023.
  Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho.

• [C.45] LVM-Med: Learning large-Scale self-Supervised vision models for medical imaging via second-order graph matching. Advances in NeurIPS, 2023.
  Duy Nguyen, Hoang Nguyen, Nghiem Diep, Tan Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Mathias Niepert, Daniel Sonntag.

• [C.44] On excess mass behavior in Gaussian mixture models with Orlicz Wasserstein distances . Proceedings of the ICML, 2023.
  Aritra Guha, Nhat Ho, Long Nguyen.

• [C.43] Neural collapse in deep linear network: from balanced to imbalanced data . Proceedings of the ICML, 2023.
  Hien Dang*, Tan Nguyen*, Tho Tran, Stanley Osher, Hung Tran, Nhat Ho†.

• [C.42] Self-attention amortized distributional projection optimization for sliced Wasserstein point-clouds reconstruction . Proceedings of the ICML, 2023.
  Khai Nguyen*, Dang Nguyen*, Nhat Ho.

• [C.41] Revisiting over-smoothing and over-squashing using Ollivier's Ricci curvature . Proceedings of the ICML, 2023.
  Khang Nguyen, Tan Minh Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho†, Stanley Osher†.

• [C.40] Hierarchical sliced Wasserstein distance. ICLR, 2023.
  Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Nguyen, Nhat Ho.

• [C.39] A primal-dual framework for transformers and neural networks. ICLR, 2023 (Spotlight).
  Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard Baraniuk, Stanley Osher.

• [C.38] Global-local regularization via distributional robustness. AISTATS, 2023.
  Hoang Phan, Trung Le, Trung Phung, Tuan Anh Bui, Nhat Ho, Dinh Phung.

• [C.37] Model fusion of heterogeneous neural networks via cross-layer alignment . IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 (Top 3%).
  Dang Nguyen, Trang Nguyen, Khai Nguyen, Dinh Phung, Hung Bui, Nhat Ho.

• [C.36] A probabilistic framework for pruning transformers via a finite admixture of keys . IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 (Top 3%).
  Tam Nguyen*, Tan Nguyen*, Long Bui, Hai Do, Dung Le, Hung Tran-The, Khuong Nguyen, Nhat Ho, Stanley Osher, Richard Baraniuk.

• [C.35] Joint self-supervised image-volume representation learning with intra-inter contrastive clustering . AAAI, 2023.
  Duy Nguyen, Nguyen Hoang, Truong Mai, Cao Tri, Binh Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag.

• [C.34] Efficient forecasting of large scale hierarchical time series via multilevel clustering . International conference on Time Series and Forecasting (ITISE), 2023.
  Xing Han, Tongzheng Ren, Jing Hu, Joydeep Ghosh, Nhat Ho.

• [C.33] Revisiting projected Wasserstein metric on images: from vectorization to convolution. Advances in NeurIPS, 2022.
  Khai Nguyen, Nhat Ho.

• [C.32] FourierFormer: Transformer meets generalized Fourier integral attentions . Advances in NeurIPS, 2022.
  Tan Nguyen*, Minh Pham*, Tam Nguyen, Khai Nguyen, Stanley Osher, Nhat Ho.

• [C.31] Amortized projection optimization for sliced Wasserstein generative models. Advances in NeurIPS, 2022.
  Khai Nguyen, Nhat Ho.

• [C.30] Improving Transformer with an admixture of attention heads . Advances in NeurIPS, 2022.
  Tam Nguyen*, Tan Nguyen*, Hai Do, Khai Nguyen, Vishwanath Saragadam, Minh Pham, Khuong Nguyen,Stanley Osher†, Nhat Ho†.

• [C.29] Beyond black box densities: Parameter learning for the deviated components. Advances in NeurIPS, 2022.
  Dat Do*, Nhat Ho*, Long Nguyen.

• [C.28] Stochastic multiple target sampling gradient descent . Advances in NeurIPS, 2022.
  Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung.

• [C.27] Refined convergence rates for maximum likelihood estimation under finite mixture models. Proceedings of the ICML, 2022 (Long Presentation).
  Tudor Manole, Nhat Ho.

• [C.26] Improving Transformers with probabilistic attention keys . Proceedings of the ICML, 2022.
  Tam Nguyen*, Tan Nguyen*, Dung Le, Khuong Nguyen, Anh Tran, Richard Baraniuk, Stanley Osher†, Nhat Ho†.

• [C.25] Entropic Gromov-Wasserstein between Gaussian distributions. Proceedings of the ICML, 2022.
  Khang Le*, Dung Le*, Huy Nguyen*, Dat Do, Tung Pham, Nhat Ho.

• [C.24] Improving minibatch optimal transport via partial transportation. Proceedings of the ICML, 2022.
  Khai Nguyen, Dang Nguyen, Anh Le, Tung Pham, Nhat Ho.

• [C.23] On transportation of mini-batches: A hierarchical approach . Proceedings of the ICML, 2022.
  Khai Nguyen, Dang Nguyen, Quoc Nguyen, Tung Pham, Dinh Phung, Hung Bui, Trung Le, Nhat Ho.

• [C.22] Architecture agnostic federated learning for neural networks. Proceedings of the ICML, 2022.
  Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh.

• [C.21] Towards statistical and computational complexities of Polyak step size gradient descent . AISTATS, 2022.
  Tongzheng Ren*, Fuheng Cui*, Alexia Atsidakou*, Sujay Sanghavi, Nhat Ho.

• [C.20] On multimarginal partial optimal transport: Equivalent forms and computational complexity. AISTATS, 2022.
  Khang Le*, Huy Nguyen*, Tung Pham, Nhat Ho.

• [C.19] On structured filtering-clustering: Global error bound and optimal first-order algorithms. AISTATS, 2022.
  Nhat Ho*, Tianyi Lin*, Michael I. Jordan.

• [C.18] Weak separation in mixture models and implications for principal stratification. AISTATS, 2022.
  Nhat Ho**, Avi Feller**, Evan Greif**, Luke W. Miratrix**, Natesh S. Pillai**.

• [C.17] On robust optimal transport: Computational complexity and barycenter computation . Advances in NeurIPS, 2021.
  Khang Le*, Huy Nguyen*, Quang Nguyen, Tung Pham, Hung Bui†, Nhat Ho†.

• [C.16] Structured Dropout variational inference for Bayesian neural networks . Advances in NeurIPS, 2021.
  Son Nguyen*, Duong Nguyen*, Khai Nguyen, Khoat Than, Hung Bui†, Nhat Ho†.

• [C.15] LAMDA: Label matching deep domain adaptation. Proceedings of the ICML, 2021.
  Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung.

• [C.14] Point-set distances for learning representations of 3D point clouds. International Conference on Computer Vision (ICCV), 2021.
  Trung Nguyen, Hieu Pham, Tam Le, Tung Pham, Nhat Ho, Son Hua.

• [C.13] On the minimax optimality of the EM algorithm for learning two-component mixed linear regression. AISTATS, 2021.
  Jeong Y. Kwon, Nhat Ho, Constantine Caramanis.

• [C.12] Flow-based alignment approaches for probability measures in different spaces. AISTATS, 2021.
  Tam Le*, Nhat Ho*, Makoto Yamada.

• [C.11] Distributional sliced-Wasserstein and applications to deep generative modeling. ICLR, 2021 (Spotlight).
  Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui.

• [C.10] Improving relational regularized autoencoders with spherical sliced fused Gromov Wasserstein. ICLR, 2021.
  Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui.

• [C.9] Projection robust Wasserstein distance and Riemannian optimization . Advances in NeurIPS, 2020 (Spotlight).
  Tianyi Lin*, Chenyou Fan*, Nhat Ho, Marco Cuturi, Michael I. Jordan.

• [C.8] Fixed-support Wasserstein barycenters: computational hardness and fast algorithm . Advances in NeurIPS, 2020.
  Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan.

• [C.7] On unbalanced optimal transport: an analysis of Sinkhorn algorithm. Proceedings of the ICML, 2020.
  Khiem Pham*, Khang Le*, Nhat Ho, Tung Pham, Hung Bui.

• [C.6] Sharp analysis of Expectation-Maximization for weakly identifiable models. AISTATS, 2020.
  Raaz Dwivedi*, Nhat Ho*, Koulik Khamaru*, Martin J. Wainwright, Michael I. Jordan, Bin Yu.

• [C.5] Fast algorithms for computational optimal transport and Wasserstein barycenter. AISTATS, 2020.
  Wenshuo Guo, Nhat Ho, Michael I. Jordan.

• [C.4] On efficient optimal transport: an analysis of greedy and accelerated mirror descent algorithms. Proceedings of the ICML, 2019.
  Tianyi Lin*, Nhat Ho*, Michael I. Jordan.

• [C.3] Probabilistic multilevel clustering via composite transportation distance. AISTATS, 2019.
  Nhat Ho*, Viet Huynh*, Dinh Phung, Michael I. Jordan.

• [C.2] Theoretical guarantees for EM under misspecified Gaussian mixture models . Advances in NeurIPS, 2018.
  Raaz Dwivedi*, Nhat Ho*, Koulik Khamaru*, Martin J. Wainwright, Michael I. Jordan.

• [C.1] Multilevel clustering via Wasserstein means. Proceedings of the ICML, 2017.
  Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, and Dinh Phung.

Preprints

• Multivariate smoothing via the Fourier integral theorem and Fourier kernel . arXiv preprint.
  Nhat Ho**, Stephen G. Walker**.

• Beyond EM algorithm on over-specified two-component location-scale Gaussian mixtures. arXiv preprint.
  Tongzheng Ren*, Fuheng Cui*, Sujay Sanghavi, Nhat Ho.

• On posterior inference for the number of clusters in Dirichlet process mixture models. arXiv preprint.
  Chiao-Yu Yang, Eric Xia, Nhat Ho, Michael I. Jordan.

• Sampling for Bayesian mixture models: MCMC with polynomial-time mixing. arXiv preprint.
  Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan.

• Uniform convergence rates for maximum likelihood estimation under two-component Gaussian mixture models. Arxiv Preprint.
  Tudor Manole, Nhat Ho.

• BONuS: Multiple multivariate testing with a data-adaptive test statistic . Arxiv Preprint.
  Chiao-Yu Yang, Lihua Lei, Nhat Ho, Will Fithian.

Phd Thesis

• Parameter Estimation and Multilevel Clustering with Mixture and Hierarchical Models . Phd Thesis, University of Michigan, Ann Arbor. Advisors: Professor Long Nguyen and Professor Ya'acov Ritov.