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Xiuyuan Cheng
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2020 – today
- 2024
- [j14]Chen Xu
, Jonghyeok Lee, Xiuyuan Cheng
Flow-Based Distributionally Robust Optimization. IEEE J. Sel. Areas Inf. Theory 5: 62-77 (2024) - [j13]Xiuyuan Cheng
Convergence of Flow-Based Generative Models via Proximal Gradient Descent in Wasserstein Space. IEEE Trans. Inf. Theory 70(11): 8087-8106 (2024) - [c19]Matthew Repasky
Stage-Regularized Neural Stein Critics For Testing Goodness-Of-Fit Of Generative Models. ICASSP 2024: 7255-7259 - [i46]Varun Khurana, Xiuyuan Cheng, Alexander Cloninger:
Training Guarantees of Neural Network Classification Two-Sample Tests by Kernel Analysis. CoRR abs/2407.04806 (2024) - [i45]Vishal Purohit, Matthew Repasky, Jianfeng Lu, Qiang Qiu, Yao Xie, Xiuyuan Cheng:
Posterior sampling via Langevin dynamics based on generative priors. CoRR abs/2410.02078 (2024) - [i44]Chen Xu, Xiuyuan Cheng, Yao Xie:
Local Flow Matching Generative Models. CoRR abs/2410.02548 (2024) - [i43]Yixuan Tan, Xiuyuan Cheng:
Improved convergence rate of kNN graph Laplacians. CoRR abs/2410.23212 (2024) - 2023
- [j12]Boris Landa, Xiuyuan Cheng:
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling. SIAM J. Math. Data Sci. 5(3): 589-614 (2023) - [j11]Matthew Repasky
Neural Stein Critics With Staged L 2-Regularization. IEEE Trans. Inf. Theory 69(11): 7246-7275 (2023) - [c18]Junghwan Lee, Yao Xie, Xiuyuan Cheng:
Training Neural Networks for Sequential Change-Point Detection. ICASSP 2023: 1-5 - [c17]Zheng Dong, Xiuyuan Cheng, Yao Xie:
Spatio-temporal point processes with deep non-stationary kernels. ICLR 2023 - [c16]Chen Xu, Xiuyuan Cheng, Yao Xie:
Normalizing flow neural networks by JKO scheme. NeurIPS 2023 - [i42]Eitan Rosen, Xiuyuan Cheng, Yoel Shkolnisky:
The G-invariant graph Laplacian. CoRR abs/2303.17001 (2023) - [i41]Chen Xu, Xiuyuan Cheng, Yao Xie:
Optimal transport flow and infinitesimal density ratio estimation. CoRR abs/2305.11857 (2023) - [i40]Yixuan Tan, Liyan Xie, Xiuyuan Cheng:
Neural Differential Recurrent Neural Network with Adaptive Time Steps. CoRR abs/2306.01674 (2023) - [i39]Eitan Rosen, Xiuyuan Cheng, Yoel Shkolnisky:
G-invariant diffusion maps. CoRR abs/2306.07350 (2023) - [i38]Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie:
Deep graph kernel point processes. CoRR abs/2306.11313 (2023) - [i37]Xiuyuan Cheng, Jianfeng Lu, Yixin Tan, Yao Xie:
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space. CoRR abs/2310.17582 (2023) - [i36]Chen Xu, Jonghyeok Lee, Xiuyuan Cheng, Yao Xie:
Flow-based Distributionally Robust Optimization. CoRR abs/2310.19253 (2023) - 2022
- [j10]Wei Zhu, Qiang Qiu, A. Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng:
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters. J. Mach. Learn. Res. 23: 68:1-68:45 (2022) - [j9]Chen Xu
Invertible Neural Networks for Graph Prediction. IEEE J. Sel. Areas Inf. Theory 3(3): 454-467 (2022) - [j8]Xiuyuan Cheng, Alexander Cloninger
Classification Logit Two-Sample Testing by Neural Networks for Differentiating Near Manifold Densities. IEEE Trans. Inf. Theory 68(10): 6631-6662 (2022) - [c15]Shixiang Zhu, Haoyun Wang, Zheng Dong, Xiuyuan Cheng, Yao Xie:
Neural Spectral Marked Point Processes. ICLR 2022 - [c14]Ziyu Chen, Yingzhou Li, Xiuyuan Cheng:
SpecNet2: Orthogonalization-free Spectral Embedding by Neural Networks. MSML 2022: 33-48 - [i35]Sarah Huestis-Mitchell, Xiuyuan Cheng, Yao Xie:
Police Text Analysis: Topic Modeling and Spatial Relative Density Estimation. CoRR abs/2202.04176 (2022) - [i34]Chen Xu, Xiuyuan Cheng, Yao Xie:
Training neural networks using monotone variational inequality. CoRR abs/2202.08876 (2022) - [i33]Chen Xu, Xiuyuan Cheng, Yao Xie:
Invertible Neural Networks for Graph Prediction. CoRR abs/2206.01163 (2022) - [i32]Ziyu Chen, Yingzhou Li, Xiuyuan Cheng:
SpecNet2: Orthogonalization-free spectral embedding by neural networks. CoRR abs/2206.06644 (2022) - [i31]Xiuyuan Cheng, Boris Landa:
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise. CoRR abs/2206.11386 (2022) - [i30]Matthew Repasky, Xiuyuan Cheng, Yao Xie:
Neural Stein critics with staged L2-regularization. CoRR abs/2207.03406 (2022) - [i29]Boris Landa, Xiuyuan Cheng:
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling. CoRR abs/2209.08004 (2022) - [i28]Junghwan Lee, Yao Xie, Xiuyuan Cheng:
Training Neural Networks for Sequential Change-point Detection. CoRR abs/2210.17312 (2022) - [i27]Zheng Dong, Xiuyuan Cheng, Yao Xie:
Spatio-temporal point processes with deep non-stationary kernels. CoRR abs/2211.11179 (2022) - [i26]Chen Xu, Xiuyuan Cheng, Yao Xie:
Invertible normalizing flow neural networks by JKO scheme. CoRR abs/2212.14424 (2022) - 2021
- [c13]Yixing Zhang, Xiuyuan Cheng, Galen Reeves:
Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples. AISTATS 2021: 2422-2430 - [c12]Xiuyuan Cheng, Zichen Miao, Qiang Qiu:
Graph Convolution with Low-rank Learnable Local Filters. ICLR 2021 - [c11]Zichen Miao, Ze Wang, Xiuyuan Cheng, Qiang Qiu:
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks. NeurIPS 2021: 3376-3388 - [c10]Xiuyuan Cheng, Yao Xie:
Neural Tangent Kernel Maximum Mean Discrepancy. NeurIPS 2021: 6658-6670 - [i25]Xiuyuan Cheng, Nan Wu:
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation. CoRR abs/2101.09875 (2021) - [i24]Yixing Zhang, Xiuyuan Cheng, Galen Reeves:
Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples. CoRR abs/2103.00394 (2021) - [i23]Xiuyuan Cheng, Yao Xie:
Kernel MMD Two-Sample Tests for Manifold Data. CoRR abs/2105.03425 (2021) - [i22]Xiuyuan Cheng, Yao Xie:
Neural Tangent Kernel Maximum Mean Discrepancy. CoRR abs/2106.03227 (2021) - [i21]Shixiang Zhu, Haoyun Wang, Xiuyuan Cheng, Yao Xie:
Neural Spectral Marked Point Processes. CoRR abs/2106.10773 (2021) - 2020
- [j7]Hrushikesh N. Mhaskar, Xiuyuan Cheng, Alexander Cloninger:
A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials. Frontiers Appl. Math. Stat. 6: 31 (2020) - [j6]Xiuyuan Cheng
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. SIAM J. Imaging Sci. 13(2): 1015-1048 (2020) - [c9]Henry Li, Ofir Lindenbaum, Xiuyuan Cheng, Alexander Cloninger:
Variational Diffusion Autoencoders with Random Walk Sampling. ECCV (23) 2020: 362-378 - [c8]Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
Stochastic Conditional Generative Networks with Basis Decomposition. ICLR 2020 - [c7]Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng:
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization. MSML 2020: 431-450 - [c6]Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
A Dictionary Approach to Domain-Invariant Learning in Deep Networks. NeurIPS 2020 - [i20]Xiuyuan Cheng, Zichen Miao, Qiang Qiu:
Graph Neural Networks with Low-rank Learnable Local Filters. CoRR abs/2008.01818 (2020) - [i19]Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution. CoRR abs/2009.02386 (2020) - [i18]Xiuyuan Cheng, Hau-Tieng Wu:
Convergence of Graph Laplacian with kNN Self-tuned Kernels. CoRR abs/2011.01479 (2020)
2010 – 2019
- 2019
- [c5]Xiuyuan Cheng, Qiang Qiu, A. Robert Calderbank, Guillermo Sapiro:
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks. ICLR (Poster) 2019 - [i17]Hrushikesh N. Mhaskar, Alex Cloninger, Xiuyuan Cheng:
A witness function based construction of discriminative models using Hermite polynomials. CoRR abs/1901.02975 (2019) - [i16]Henry Li
Diffusion Variational Autoencoders. CoRR abs/1905.12724 (2019) - [i15]Wei Zhu, Qiang Qiu, A. Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng:
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters. CoRR abs/1909.11193 (2019) - [i14]Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
Domain-invariant Learning using Adaptive Filter Decomposition. CoRR abs/1909.11285 (2019) - [i13]Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
Stochastic Conditional Generative Networks with Basis Decomposition. CoRR abs/1909.11286 (2019) - [i12]Xiuyuan Cheng, Alexander Cloninger:
Classification Logit Two-sample Testing by Neural Networks. CoRR abs/1909.11298 (2019) - [i11]Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng:
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization. CoRR abs/1912.04154 (2019) - 2018
- [c4]Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng:
Provable Estimation of the Number of Blocks in Block Models. AISTATS 2018: 1185-1194 - [c3]Qiang Qiu, Xiuyuan Cheng, A. Robert Calderbank, Guillermo Sapiro:
DCFNet: Deep Neural Network with Decomposed Convolutional Filters. ICML 2018: 4195-4204 - [i10]Qiang Qiu, Xiuyuan Cheng, A. Robert Calderbank, Guillermo Sapiro:
DCFNet: Deep Neural Network with Decomposed Convolutional Filters. CoRR abs/1802.04145 (2018) - [i9]Uri Shaham, James Garritano
Defending against Adversarial Images using Basis Functions Transformations. CoRR abs/1803.10840 (2018) - [i8]Xiuyuan Cheng, Qiang Qiu, A. Robert Calderbank, Guillermo Sapiro:
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks. CoRR abs/1805.06846 (2018) - [i7]Yingzhou Li, Xiuyuan Cheng, Jianfeng Lu:
Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks. CoRR abs/1805.07451 (2018) - [i6]Xiuyuan Cheng, Gal Mishne:
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. CoRR abs/1810.10695 (2018) - 2017
- [i5]Xiuyuan Cheng, Alexander Cloninger, Ronald R. Coifman:
Two-sample Statistics Based on Anisotropic Kernels. CoRR abs/1709.05006 (2017) - 2016
- [j5]Teng Zhang, Xiuyuan Cheng, Amit Singer
Marčenko-Pastur law for Tyler's M-estimator. J. Multivar. Anal. 149: 114-123 (2016) - [j4]Gabi Pragier
A Graph Partitioning Approach to Simultaneous Angular Reconstitution. IEEE Trans. Computational Imaging 2(3): 323-334 (2016) - [c2]Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger:
A Deep Learning Approach to Unsupervised Ensemble Learning. ICML 2016: 30-39 - [i4]Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger:
A Deep Learning Approach to Unsupervised Ensemble Learning. CoRR abs/1602.02285 (2016) - 2015
- [j3]Zitong Chen, Yubao Liu, Raymond Chi-Wing Wong, Jiamin Xiong, Xiuyuan Cheng, Peihuan Chen:
Rotating MaxRS queries. Inf. Sci. 305: 110-129 (2015) - [i3]Xiuyuan Cheng, Xu Chen, Stéphane Mallat:
Deep Haar Scattering Networks. CoRR abs/1509.09187 (2015) - 2014
- [j2]Nicolas Boumal
Concentration of the Kirchhoff index for Erdős-Rényi graphs. Syst. Control. Lett. 74: 74-80 (2014) - [c1]Xu Chen, Xiuyuan Cheng, Stéphane Mallat:
Unsupervised Deep Haar Scattering on Graphs. NIPS 2014: 1709-1717 - [i2]Xu Chen, Xiuyuan Cheng, Stéphane Mallat:
Unsupervised Learning by Deep Scattering Contractions. CoRR abs/1406.2390 (2014) - 2013
- [i1]Nicolas Boumal, Xiuyuan Cheng:
Expected performance bounds for estimation on graphs from random relative measurements. CoRR abs/1307.6398 (2013) - 2010
- [j1]Ling Lin, Xiuyuan Cheng, Weinan E, An-Chang Shi, Pingwen Zhang:
A numerical method for the study of nucleation of ordered phases. J. Comput. Phys. 229(5): 1797-1809 (2010)
Coauthor Index
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last updated on 2024-12-03 20:28 CET by the dblp team
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