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2020 – today
- 2024
[j9]Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing. J. Mach. Learn. Res. 25: 169:1-169:47 (2024)- [j8]Wenlong Mou, Nhat Ho, Martin J. Wainwright
, Peter L. Bartlett, Michael I. Jordan:
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. 6(2): 553-577 (2024) - [j7]Jeongyeol Kwon, Wei Qian, Yudong Chen, Constantine Caramanis, Damek Davis, Nhat Ho:
Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. IEEE Trans. Inf. Theory 70(9): 6519-6546 (2024) - [j6]Qiujiang Jin, Tongzheng Ren, Nhat Ho, Aryan Mokhtari:
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models. Trans. Mach. Learn. Res. 2024 (2024) - [c72]Huy Nguyen, Khai Nguyen, Nhat Ho:
On Parameter Estimation in Deviated Gaussian Mixture of Experts. AISTATS 2024: 2674-2682 - [c71]Huy Nguyen, TrungTin Nguyen, Khai Nguyen, Nhat Ho:
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. AISTATS 2024: 2683-2691 - [c70]Tung Le, Khai Nguyen, Shanlin Sun, Nhat Ho, Xiaohui Xie:
Integrating Efficient Optimal Transport and Functional Maps for Unsupervised Shape Correspondence Learning. CVPR 2024: 23188-23198 - [c69]Dung Le, Huy Nguyen, Khai Nguyen, Trang Nguyen, Nhat Ho:
Fast Approximation of the Generalized Sliced-Wasserstein Distance. ICASSP 2024: 6920-6924 - [c68]Hien Dang, Tho Tran Huu, Tan Minh Nguyen, Nhat Ho:
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders. ICLR 2024 - [c67]Thanh-Tung Le, Khai Nguyen, Shanlin Sun, Kun Han, Nhat Ho, Xiaohui Xie:
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction. ICLR 2024 - [c66]Manh Luong, Khai Nguyen, Nhat Ho, Gholamreza Haffari, Dinh Phung, Lizhen Qu:
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation. ICLR 2024 - [c65]Huy Nguyen, Pedram Akbarian, Fanqi Yan, Nhat Ho:
Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts. ICLR 2024 - [c64]Khai Nguyen, Nicola Bariletto, Nhat Ho:
Quasi-Monte Carlo for 3D Sliced Wasserstein. ICLR 2024 - [c63]Khai Nguyen, Nhat Ho:
Sliced Wasserstein Estimation with Control Variates. ICLR 2024 - [c62]Hien Dang, Tho Tran Huu, Tan Minh Nguyen, Nhat Ho:
Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model. ICML 2024 - [c61]Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho:
Improving Computational Complexity in Statistical Models with Local Curvature Information. ICML 2024 - [c60]Huy Nguyen, Pedram Akbarian, Nhat Ho:
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts? ICML 2024 - [c59]Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, Nhat Ho:
A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts. ICML 2024 - [c58]Huy Nguyen, Nhat Ho, Alessandro Rinaldo:
On Least Square Estimation in Softmax Gating Mixture of Experts. ICML 2024 - [c57]Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert:
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. ICML 2024 - [c56]Khai Nguyen, Shujian Zhang, Tam Le, Nhat Ho:
Sliced Wasserstein with Random-Path Projecting Directions. ICML 2024 - [c55]Nicola Bariletto, Nhat Ho:
Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization. NeurIPS 2024 - [c54]Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria:
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion. NeurIPS 2024 - [c53]Minh Le, An Nguyen The, Huy Nguyen, Trang Nguyen, Trang Pham, Linh Ngo, Nhat Ho:
Mixture of Experts Meets Prompt-Based Continual Learning. NeurIPS 2024 - [c52]Disha Makhija, Joydeep Ghosh, Nhat Ho:
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings. NeurIPS 2024 - [c51]Khai Nguyen, Nhat Ho:
Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions. NeurIPS 2024 - [c50]Huy Nguyen, Nhat Ho, Alessandro Rinaldo:
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts. NeurIPS 2024 - [i96]Hien Dang, Tho Tran, Tan Nguyen, Nhat Ho:
Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Feature Model. CoRR abs/2401.02058 (2024) - [i95]Huy Nguyen, Pedram Akbarian, Nhat Ho:
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts? CoRR abs/2401.13875 (2024) - [i94]Nicola Bariletto, Nhat Ho:
Bayesian Nonparametrics Meets Data-Driven Robust Optimization. CoRR abs/2401.15771 (2024) - [i93]Khai Nguyen, Shujian Zhang, Tam Le, Nhat Ho:
Sliced Wasserstein with Random-Path Projecting Directions. CoRR abs/2401.15889 (2024) - [i92]Duy M. H. Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert:
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. CoRR abs/2402.01975 (2024) - [i91]Quang Pham, Giang Do, Huy Nguyen, TrungTin Nguyen, Chenghao Liu, Mina Sartipi, Binh T. Nguyen, Savitha Ramasamy, Xiaoli Li, Steven C. H. Hoi, Nhat Ho:
CompeteSMoE - Effective Training of Sparse Mixture of Experts via Competition. CoRR abs/2402.02526 (2024) - [i90]Huy Nguyen, Nhat Ho, Alessandro Rinaldo:
On Least Squares Estimation in Softmax Gating Mixture of Experts. CoRR abs/2402.02952 (2024) - [i89]Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria:
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion. CoRR abs/2402.03226 (2024) - [i88]Huy Nguyen, Khai Nguyen, Nhat Ho:
On Parameter Estimation in Deviated Gaussian Mixture of Experts. CoRR abs/2402.05220 (2024) - [i87]Tung Le, Khai Nguyen, Shanlin Sun, Nhat Ho, Xiaohui Xie:
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning. CoRR abs/2403.01781 (2024) - [i86]Khai Nguyen, Nhat Ho:
Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions. CoRR abs/2404.15378 (2024) - [i85]Khai Nguyen, Hai Nguyen, Nhat Ho:
Marginal Fairness Sliced Wasserstein Barycenter. CoRR abs/2405.07482 (2024) - [i84]Manh Luong, Khai Nguyen, Nhat Ho, Gholamreza Haffari, Dinh Q. Phung, Lizhen Qu:
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation. CoRR abs/2405.10084 (2024) - [i83]Nicola Bariletto, Khai Nguyen, Nhat Ho:
Borrowing Strength in Distributionally Robust Optimization via Hierarchical Dirichlet Processes. CoRR abs/2405.13160 (2024) - [i82]Huy Nguyen, Nhat Ho, Alessandro Rinaldo:
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts. CoRR abs/2405.13997 (2024) - [i81]Minh Le, An Nguyen, Huy Nguyen, Trang Nguyen, Trang Pham, Linh Van Ngo, Nhat Ho:
Mixture of Experts Meets Prompt-Based Continual Learning. CoRR abs/2405.14124 (2024) - [i80]Huy Nguyen, Pedram Akbarian, Trang Pham, Trang Nguyen, Shujian Zhang, Nhat Ho:
Statistical Advantages of Perturbing Cosine Router in Sparse Mixture of Experts. CoRR abs/2405.14131 (2024) - [i79]Tan M. Nguyen, Tam Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. CoRR abs/2406.13781 (2024) - [i78]Trang Nguyen, Anh Tran, Nhat Ho:
Backdoor Attack in Prompt-Based Continual Learning. CoRR abs/2406.19753 (2024) - [i77]Minh Le, Chau Nguyen, Huy Nguyen, Quyen Tran, Trung Le, Nhat Ho:
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts. CoRR abs/2410.02200 (2024) - [i76]Duy M. H. Nguyen, Nghiem T. Diep, Trung Q. Nguyen, Hoang-Bao Le, Tai Nguyen, Tien Nguyen, TrungTin Nguyen, Nhat Ho, Pengtao Xie, Roger Wattenhofer, James Zhou, Daniel Sonntag, Mathias Niepert:
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model. CoRR abs/2410.02615 (2024) - [i75]Huy Nguyen, Xing Han, Carl Harris, Suchi Saria, Nhat Ho:
On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions. CoRR abs/2410.02935 (2024) - [i74]Tuan Truong, Quyen Tran, Quan Pham-Ngoc, Nhat Ho, Dinh Phung, Trung Le:
Improving Generalization with Flat Hilbert Bayesian Inference. CoRR abs/2410.04196 (2024) - [i73]Quyen Tran, Minh Le, Tuan Truong, Dinh Phung, Linh Ngo, Thien Nguyen, Nhat Ho, Trung Le:
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning. CoRR abs/2410.04327 (2024) - [i72]Ngoc-Hai Nguyen, Dung Le, Hoang-Phi Nguyen, Tung Pham, Nhat Ho:
On Barycenter Computation: Semi-Unbalanced Optimal Transport-based Method on Gaussians. CoRR abs/2410.08117 (2024) - [i71]Pedram Akbarian, Huy Nguyen, Xing Han, Nhat Ho:
Quadratic Gating Functions in Mixture of Experts: A Statistical Insight. CoRR abs/2410.11222 (2024) - [i70]Fanqi Yan, Huy Nguyen, Dung Le, Pedram Akbarian, Nhat Ho:
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts. CoRR abs/2410.12258 (2024) - [i69]Yichen Xie, Chenfeng Xu, Chensheng Peng, Shuqi Zhao, Nhat Ho, Alexander T. Pham, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan:
X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios. CoRR abs/2411.01123 (2024) - 2023
- [c49]Duy M. H. Nguyen, Hoang Nguyen, Truong Thanh Nhat Mai, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag:
Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering. AAAI 2023: 14426-14435 - [c48]Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Q. Phung:
Global-Local Regularization Via Distributional Robustness. AISTATS 2023: 7644-7664 - [c47]Tan M. Nguyen, Tam Nguyen, Long Bui, Hai Do, Duy Khuong Nguyen, Dung D. Le, Hung Tran-The, Nhat Ho, Stanley J. Osher, Richard G. Baraniuk:
A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys. ICASSP 2023: 1-5 - [c46]Dang Nguyen, Trang Nguyen, Khai Nguyen, Dinh Q. Phung, Hung Hai Bui, Nhat Ho:
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks. ICASSP 2023: 1-5 - [c45]Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. ICLR 2023 - [c44]Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Minh Nguyen, Nhat Ho:
Hierarchical Sliced Wasserstein Distance. ICLR 2023 - [c43]Hien Dang, Tho Tran Huu, Stanley J. Osher, Hung Tran-The, Nhat Ho, Tan Minh Nguyen:
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. ICML 2023: 6873-6947 - [c42]Aritra Guha, Nhat Ho, XuanLong Nguyen:
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances. ICML 2023: 11847-11870 - [c41]Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley J. Osher, Tan Minh Nguyen:
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature. ICML 2023: 25956-25979 - [c40]Khai Nguyen, Dang Nguyen, Nhat Ho:
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction. ICML 2023: 26008-26030 - [c39]Dat Do, Huy Nguyen, Khai Nguyen, Nhat Ho:
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models. NeurIPS 2023 - [c38]Xing Han, Tongzheng Ren, Tan Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho:
Designing Robust Transformers using Robust Kernel Density Estimation. NeurIPS 2023 - [c37]Khai Nguyen, Nhat Ho:
Energy-Based Sliced Wasserstein Distance. NeurIPS 2023 - [c36]Duy M. H. Nguyen, Hoang Nguyen, Nghiem Tuong Diep, Tan Ngoc Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert:
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. NeurIPS 2023 - [c35]Huy Nguyen, TrungTin Nguyen, Nhat Ho:
Demystifying Softmax Gating Function in Gaussian Mixture of Experts. NeurIPS 2023 - [c34]Khai Nguyen, Tongzheng Ren, Nhat Ho:
Markovian Sliced Wasserstein Distances: Beyond Independent Projections. NeurIPS 2023 - [i68]Hien Dang, Tho Tran, Tan M. Nguyen, Stanley J. Osher, Hung Tran-The, Nhat Ho:
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. CoRR abs/2301.00437 (2023) - [i67]Khai Nguyen, Tongzheng Ren, Nhat Ho:
Markovian Sliced Wasserstein Distances: Beyond Independent Projections. CoRR abs/2301.03749 (2023) - [i66]Khai Nguyen, Dang Nguyen, Nhat Ho:
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction. CoRR abs/2301.04791 (2023) - [i65]Khai Nguyen, Nhat Ho:
Energy-Based Sliced Wasserstein Distance. CoRR abs/2304.13586 (2023) - [i64]Khai Nguyen, Nhat Ho:
Control Variate Sliced Wasserstein Estimators. CoRR abs/2305.00402 (2023) - [i63]Huy Nguyen, TrungTin Nguyen, Nhat Ho:
Demystifying Softmax Gating in Gaussian Mixture of Experts. CoRR abs/2305.03288 (2023) - [i62]Huy Nguyen, TrungTin Nguyen, Khai Nguyen, Nhat Ho:
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. CoRR abs/2305.07572 (2023) - [i61]Tung Le, Khai Nguyen, Shanlin Sun, Kun Han, Nhat Ho, Xiaohui Xie:
Diffeomorphic Deformation via Sliced Wasserstein Distance Optimization for Cortical Surface Reconstruction. CoRR abs/2305.17555 (2023) - [i60]Hien Dang, Tho Tran, Tan Nguyen, Nhat Ho:
Posterior Collapse in Linear Conditional and Hierarchical Variational Autoencoders. CoRR abs/2306.05023 (2023) - [i59]Disha Makhija, Joydeep Ghosh, Nhat Ho:
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings. CoRR abs/2306.07959 (2023) - [i58]Duy M. H. Nguyen, Hoang Nguyen, Nghiem Tuong Diep, Tan Ngoc Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert:
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. CoRR abs/2306.11925 (2023) - [i57]Khai Nguyen, Nicola Bariletto, Nhat Ho:
Quasi-Monte Carlo for 3D Sliced Wasserstein. CoRR abs/2309.11713 (2023) - [i56]Huy Nguyen, Pedram Akbarian, Fanqi Yan, Nhat Ho:
Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts. CoRR abs/2309.13850 (2023) - [i55]Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, Nhat Ho:
A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts. CoRR abs/2310.14188 (2023) - [i54]Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep, Nghi Quoc Phan, Quang Pham, Vinh Tong, Binh T. Nguyen, Ngan Hoang Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, Mathias Niepert:
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation. CoRR abs/2311.11096 (2023) - 2022
- [j5]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. 23: 65:1-65:43 (2022) - [j4]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. J. Mach. Learn. Res. 23: 137:1-137:42 (2022) - [j3]Nhat Ho, Chiao-Yu Yang, Michael I. Jordan:
Convergence Rates for Gaussian Mixtures of Experts. J. Mach. Learn. Res. 23: 323:1-323:81 (2022) - [c33]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c32]Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho:
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. AISTATS 2022: 3930-3961 - [c31]Khang Le, Huy Nguyen, Khai Nguyen, Tung Pham, Nhat Ho:
On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity. AISTATS 2022: 4397-4413 - [c30]Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai:
Weak Separation in Mixture Models and Implications for Principal Stratification. AISTATS 2022: 5416-5458 - [c29]Khang Le, Dung Q. Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho:
Entropic Gromov-Wasserstein between Gaussian Distributions. ICML 2022: 12164-12203 - [c28]Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh:
Architecture Agnostic Federated Learning for Neural Networks. ICML 2022: 14860-14870 - [c27]Tudor A. Manole, Nhat Ho:
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models. ICML 2022: 14979-15006 - [c26]Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher:
Improving Transformers with Probabilistic Attention Keys. ICML 2022: 16595-16621 - [c25]Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho:
On Transportation of Mini-batches: A Hierarchical Approach. ICML 2022: 16622-16655 - [c24]Khai Nguyen, Dang Nguyen, The-Anh Vu-Le, Tung Pham, Nhat Ho:
Improving Mini-batch Optimal Transport via Partial Transportation. ICML 2022: 16656-16690 - [c23]Dat Do, Nhat Ho, XuanLong Nguyen:
Beyond black box densities: Parameter learning for the deviated components. NeurIPS 2022 - [c22]Tan Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley J. Osher, Nhat Ho:
FourierFormer: Transformer Meets Generalized Fourier Integral Theorem. NeurIPS 2022 - [c21]Khai Nguyen, Nhat Ho:
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution. NeurIPS 2022 - [c20]Khai Nguyen, Nhat Ho:
Amortized Projection Optimization for Sliced Wasserstein Generative Models. NeurIPS 2022 - [c19]Tan Nguyen, Tam Nguyen, Hai Do, Khai Nguyen, Vishwanath Saragadam, Minh Pham, Duy Khuong Nguyen, Nhat Ho, Stanley J. Osher:
Improving Transformer with an Admixture of Attention Heads. NeurIPS 2022 - [c18]Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung:
Stochastic Multiple Target Sampling Gradient Descent. NeurIPS 2022 - [i53]Dat Do, Nhat Ho, XuanLong Nguyen:
Beyond Black Box Densities: Parameter Learning for the Deviated Components. CoRR abs/2202.02651 (2022) - [i52]Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho:
Improving Computational Complexity in Statistical Models with Second-Order Information. CoRR abs/2202.04219 (2022) - [i51]Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh:
Architecture Agnostic Federated Learning for Neural Networks. CoRR abs/2202.07757 (2022) - [i50]Hoang Phan, Trung Le, Trung Phung, Tuan-Anh Bui, Nhat Ho, Dinh Q. Phung:
Global-Local Regularization Via Distributional Robustness. CoRR abs/2203.00553 (2022) - [i49]Khai Nguyen, Nhat Ho:
Amortized Projection Optimization for Sliced Wasserstein Generative Models. CoRR abs/2203.13417 (2022) - [i48]Khai Nguyen, Nhat Ho:
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution. CoRR abs/2204.01188 (2022) - [i47]Nhat Ho, Tongzheng Ren, Sujay Sanghavi, Purnamrita Sarkar, Rachel A. Ward:
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models. CoRR abs/2205.07999 (2022) - [i46]Tongzheng Ren, Fuheng Cui, Sujay Sanghavi, Nhat Ho:
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures. CoRR abs/2205.11078 (2022) - [i45]Disha Makhija, Nhat Ho, Joydeep Ghosh:
Federated Self-supervised Learning for Heterogeneous Clients. CoRR abs/2205.12493 (2022) - [i44]Xing Han, Tongzheng Ren, Jing Hu, Joydeep Ghosh, Nhat Ho:
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering. CoRR abs/2205.14104 (2022) - [i43]Tan M. Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley J. Osher, Nhat Ho:
Transformer with Fourier Integral Attentions. CoRR abs/2206.00206 (2022) - [i42]Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung:
Stochastic Multiple Target Sampling Gradient Descent. CoRR abs/2206.01934 (2022) - [i41]Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Nguyen, Nhat Ho:
Hierarchical Sliced Wasserstein Distance. CoRR abs/2209.13570 (2022) - [i40]Anh Do, Duy Dinh, Tan M. Nguyen, Khuong Nguyen, Stanley J. Osher, Nhat Ho:
Improving Generative Flow Networks with Path Regularization. CoRR abs/2209.15092 (2022) - [i39]Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho:
Robustify Transformers with Robust Kernel Density Estimation. CoRR abs/2210.05794 (2022) - [i38]Dung Le, Huy Nguyen, Khai Nguyen, Trang Nguyen, Nhat Ho:
Fast Approximation of the Generalized Sliced-Wasserstein Distance. CoRR abs/2210.10268 (2022) - [i37]Hoang Phan, Lam Tran, Ngoc N. Tran, Nhat Ho, Dinh Q. Phung, Trung Le:
Improving Multi-task Learning via Seeking Task-based Flat Regions. CoRR abs/2211.13723 (2022) - [i36]Khang Nguyen, Tan Nguyen, Nhat Ho, Khuong Nguyen, Hieu Nong, Vinh Nguyen:
Revisiting Over-smoothing and Over-squashing using Ollivier's Ricci Curvature. CoRR abs/2211.15779 (2022) - [i35]Duy M. H. Nguyen, Hoang Nguyen, Mai Thanh Nhat Truong, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag:
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering. CoRR abs/2212.01893 (2022) - 2021
- [j2]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On efficient multilevel Clustering via Wasserstein distances. J. Mach. Learn. Res. 22: 145:1-145:43 (2021) - [c17]Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. AISTATS 2021: 1405-1413 - [c16]Tam Le, Nhat Ho, Makoto Yamada:
Flow-based Alignment Approaches for Probability Measures in Different Spaces. AISTATS 2021: 3934-3942 - [c15]Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, Binh-Son Hua:
Point-set Distances for Learning Representations of 3D Point Clouds. ICCV 2021: 10458-10467 - [c14]Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui:
Distributional Sliced-Wasserstein and Applications to Generative Modeling. ICLR 2021 - [c13]Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui:
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein. ICLR 2021 - [c12]Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung:
LAMDA: Label Matching Deep Domain Adaptation. ICML 2021: 6043-6054 - [c11]Son Nguyen, Duong Nguyen, Khai Nguyen, Khoat Than, Hung Bui, Nhat Ho:
Structured Dropout Variational Inference for Bayesian Neural Networks. NeurIPS 2021: 15188-15202 - [c10]Khang Le, Huy Nguyen, Quang Minh Nguyen, Tung Pham, Hung Bui, Nhat Ho:
On Robust Optimal Transport: Computational Complexity and Barycenter Computation. NeurIPS 2021: 21947-21959 - [i34]Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the computational and statistical complexity of over-parameterized matrix sensing. CoRR abs/2102.02756 (2021) - [i33]Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, Binh-Son Hua:
Point-set Distances for Learning Representations of 3D Point Clouds. CoRR abs/2102.04014 (2021) - [i32]Khai Nguyen, Quoc Nguyen, Nhat Ho, Tung Pham, Hung Bui, Dinh Phung, Trung Le:
BoMb-OT: On Batch of Mini-batches Optimal Transport. CoRR abs/2102.05912 (2021) - [i31]Khang Le, Huy Nguyen, Quang Nguyen, Nhat Ho, Tung Pham, Hung Bui:
On Robust Optimal Transport: Computational Complexity, Low-rank Approximation, and Barycenter Computation. CoRR abs/2102.06857 (2021) - [i30]Son Nguyen, Duong Nguyen, Khai Nguyen, Nhat Ho, Khoat Than, Hung Bui:
Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout. CoRR abs/2102.07927 (2021) - [i29]Nhat Ho, Stephen G. Walker:
Statistical Analysis from the Fourier Integral Theorem. CoRR abs/2106.06608 (2021) - [i28]Khang Le, Huy Nguyen, Tung Pham, Nhat Ho:
On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexity. CoRR abs/2108.07992 (2021) - [i27]Khai Nguyen, Dang Nguyen, Tung Pham, Nhat Ho:
An Efficient Mini-batch Method via Partial Transportation. CoRR abs/2108.09645 (2021) - [i26]Khang Le, Dung Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho:
Entropic Gromov-Wasserstein between Gaussian Distributions. CoRR abs/2108.10961 (2021) - [i25]Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho:
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. CoRR abs/2110.07810 (2021) - [i24]Tam Nguyen, Tan M. Nguyen, Dung Le, Khuong Nguyen, Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher:
Transformer with a Mixture of Gaussian Keys. CoRR abs/2110.08678 (2021) - [i23]Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Q. Phung:
On Label Shift in Domain Adaptation via Wasserstein Distance. CoRR abs/2110.15520 (2021) - [i22]Dang Nguyen, Khai Nguyen, Dinh Phung, Hung Bui, Nhat Ho:
Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment. CoRR abs/2110.15538 (2021) - 2020
- [c9]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. AISTATS 2020: 1866-1876 - [c8]Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. AISTATS 2020: 2088-2097 - [c7]Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui:
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm. ICML 2020: 7673-7682 - [c6]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. NeurIPS 2020 - [c5]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. NeurIPS 2020 - [i21]Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui:
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm. CoRR abs/2002.03293 (2020) - [i20]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. CoRR abs/2002.04783 (2020) - [i19]Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui:
Distributional Sliced-Wasserstein and Applications to Generative Modeling. CoRR abs/2002.07367 (2020) - [i18]Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Instability, Computational Efficiency and Statistical Accuracy. CoRR abs/2005.11411 (2020) - [i17]Jeongyeol Kwon, Nhat Ho, Constantine Caramanis:
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. CoRR abs/2006.02601 (2020) - [i16]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. CoRR abs/2006.07458 (2020) - [i15]Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui:
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein. CoRR abs/2010.01787 (2020)
2010 – 2019
- 2019
- [j1]Nhat Ho, XuanLong Nguyen:
Singularity Structures and Impacts on Parameter Estimation in Finite Mixtures of Distributions. SIAM J. Math. Data Sci. 1(4): 730-758 (2019) - [c4]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. AISTATS 2019: 3149-3157 - [c3]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. ICML 2019: 3982-3991 - [i14]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. CoRR abs/1901.06482 (2019) - [i13]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Challenges with EM in application to weakly identifiable mixture models. CoRR abs/1902.00194 (2019) - [i12]Nhat Ho, Tianyi Lin, Michael I. Jordan:
Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁1-Convex Clustering. CoRR abs/1904.07462 (2019) - [i11]Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport. CoRR abs/1905.09952 (2019) - [i10]Chiao-Yu Yang, Nhat Ho, Michael I. Jordan:
Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models. CoRR abs/1905.09959 (2019) - [i9]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. CoRR abs/1906.01437 (2019) - [i8]Nhat Ho, Chiao-Yu Yang, Michael I. Jordan:
Convergence Rates for Gaussian Mixtures of Experts. CoRR abs/1907.04377 (2019) - [i7]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On Efficient Multilevel Clustering via Wasserstein Distances. CoRR abs/1909.08787 (2019) - [i6]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. CoRR abs/1910.00152 (2019) - [i5]Tam Le, Nhat Ho, Makoto Yamada:
Computationally Efficient Tree Variants of Gromov-Wasserstein. CoRR abs/1910.04462 (2019) - [i4]Tam Le, Viet Huynh, Nhat Ho, Dinh Q. Phung, Makoto Yamada:
On Scalable Variant of Wasserstein Barycenter. CoRR abs/1910.04483 (2019) - [i3]Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing. CoRR abs/1912.05153 (2019) - 2018
- [c2]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan:
Theoretical guarantees for EM under misspecified Gaussian mixture models. NeurIPS 2018: 9704-9712 - [i2]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. CoRR abs/1810.11911 (2018) - [i1]Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk:
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR abs/1811.02657 (2018) - 2017
- [c1]Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung:
Multilevel Clustering via Wasserstein Means. ICML 2017: 1501-1509
Coauthor Index
[c72] [c71] [c70] [c69] [c67] [c66] [c64] [c63] [c56] [c51] [i93] [i88] [i87] [i86] [i85] [i84] [i83] [c46] [c44] [c40] [c39] [c38] [c37] [c34] [i67] [i66] [i65] [i64] [i62] [i61] [i57] [c31] [c25] [c24] [c22] [c21] [c20] [c19] [i49] [i48] [i43] [i41] [i39] [i38] [c14] [c13] [c11] [i32] [i30] [i27] [i22] [i19] [i15]
Tan M. Nguyen
aka: Tan Minh Nguyen
aka: Tan Minh Nguyen
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