Publications of Xiuyuan Cheng    :recent first  combined  bibtex listing:

Papers Published

1. Cheng, X; Shaham, U; Dror, O; Jaffe, A; Nadler, B; Chang, J; Kluger, Y, A Deep Learning Approach to Unsupervised Ensemble Learning, Proceedings of The 33rd International Conference on Machine Learning, vol. 48 (June, 2016), pp. 30-39, PMLR
2. Pragier, G; Greenberg, I; Cheng, X; Shkolnisky, Y, A Graph Partitioning Approach to Simultaneous Angular Reconstitution, IEEE Transactions on Computational Imaging, vol. 2 no. 3 (September, 2016), pp. 323-334, Institute of Electrical and Electronics Engineers (IEEE) [doi]
3. Lin, L; Cheng, X; E, W; Shi, A-C; Zhang, P, A numerical method for the study of nucleation of ordered phases, Journal of Computational Physics, vol. 229 no. 5 (March, 2010), pp. 1797-1809, Elsevier BV [doi]
4. Mhaskar, HN; Cheng, X; Cloninger, A, A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials, Frontiers in Applied Mathematics and Statistics, vol. 6 (August, 2020) [doi]  [abs]
5. Xu, Z; Li, Y; Cheng, X, Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization, Proceedings of Machine Learning Research, vol. 107 (January, 2020), pp. 431-450  [abs]
6. Li, Y; Cheng, X; Lu, J, Butterfly-net: Optimal function representation based on convolutional neural networks, Communications in Computational Physics, vol. 28 no. 5 (November, 2020), pp. 1838-1885 [doi]  [abs]
7. Cheng, X; Cloninger, A, Classification logit two-sample testing by neural networks for differentiating near manifold densities., IEEE transactions on information theory, vol. 68 no. 10 (October, 2022), pp. 6631-6662 [doi]  [abs]
8. Boumal, N; Cheng, X, Concentration of the Kirchhoff index for Erdős–Rényi graphs, Systems & Control Letters, vol. 74 (December, 2014), pp. 74-80, Elsevier BV [doi]
9. Zhang, Y; Cheng, X; Reeves, G, Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples, Proceedings of Machine Learning Research, vol. 130 (January, 2021), pp. 2422-2430  [abs]
10. Cheng, X; Wu, H-T, Convergence of graph Laplacian with kNN self-tuned kernels, Information and Inference: A Journal of the IMA, vol. 11 no. 3 (September, 2022), pp. 889-957, Oxford University Press (OUP) [doi]  [abs]
11. Qiu, Q; Cheng, X; Calderbank, AR; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters., edited by Dy, JG; Krause, A, ICML, vol. 80 (2018), pp. 4195-4204, PMLR
12. Cheng, X; Chen, X; Mallat, S, Deep Haar scattering networks, Information and Inference, vol. 5 no. 2 (June, 2016), pp. 105-133, Oxford University Press (OUP) [doi]
13. Zhao, J; Jaffe, A; Li, H; Lindenbaum, O; Sefik, E; Jackson, R; Cheng, X; Flavell, RA; Kluger, Y, Detection of differentially abundant cell subpopulations in scRNA-seq data., Proceedings of the National Academy of Sciences of the United States of America, vol. 118 no. 22 (June, 2021), pp. e2100293118 [doi]  [abs]
14. Cheng, X; Wu, N, Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation, Applied and Computational Harmonic Analysis, vol. 61 (November, 2022), pp. 132-190 [doi]  [abs]
15. Cheng, X; Miao, Z; Qiu, Q, Graph Convolution with Low-rank Learn-able Local Filters, ICLR 2021 - 9th International Conference on Learning Representations (January, 2021)  [abs]
16. Xu, C; Cheng, X; Xie, Y, Invertible Neural Networks for Graph Prediction, IEEE Journal on Selected Areas in Information Theory, vol. 3 no. 3 (September, 2022), pp. 454-467, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
17. Zhang, T; Cheng, X; Singer, A, Marčenko–Pastur law for Tyler’s M-estimator, Journal of Multivariate Analysis, vol. 149 (July, 2016), pp. 114-123, Elsevier BV [doi]
18. Zhu, S; Wang, H; Dong, Z; Cheng, X; Xie, Y, NEURAL SPECTRAL MARKED POINT PROCESSES, ICLR 2022 - 10th International Conference on Learning Representations (January, 2022)  [abs]
19. Repasky, M; Cheng, X; Xie, Y, Neural Stein Critics with Staged L²-Regularization, IEEE Transactions on Information Theory, vol. 69 no. 11 (November, 2023), pp. 7246-7275 [doi]  [abs]
20. Cheng, X; Xie, Y, Neural Tangent Kernel Maximum Mean Discrepancy, Advances in Neural Information Processing Systems, vol. 9 (January, 2021), pp. 6658-6670, ISBN 9781713845393  [abs]
21. Cheng, X; Lin, L; E, W; Zhang, P; Shi, A-C, Nucleation of Ordered Phases in Block Copolymers, Physical Review Letters, vol. 104 no. 14 (April, 2010), American Physical Society (APS) [doi]
22. Alaifari, R; Cheng, X; Pierce, LB; Steinerberger, S, On matrix rearrangement inequalities, Proceedings of the American Mathematical Society, vol. 148 no. 5 (January, 2020), pp. 1835-1848 [doi]  [abs]
23. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph Laplacians and applications, Applied and Computational Harmonic Analysis, vol. 46 no. 3 (May, 2019), pp. 674-688, Elsevier BV [doi]
24. Lu, J; Lu, Y; Wang, X; Li, X; Linderman, GC; Wu, C; Cheng, X; Mu, L; Zhang, H; Liu, J; Su, M; Zhao, H; Spatz, ES; Spertus, JA; Masoudi, FA; Krumholz, HM; Jiang, L, Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project), The Lancet, vol. 390 no. 10112 (December, 2017), pp. 2549-2558, Elsevier BV [doi]
25. Yan, B; Sarkar, P; Cheng, X, Provable estimation of the number of blocks in block models, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS'18), vol. 84 (April, 2018), pp. 1185-1194, PMLR  [abs]
26. Landa, B; Cheng, X, Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling, SIAM Journal on Mathematics of Data Science, vol. 5 no. 3 (September, 2023), pp. 589-614, Society for Industrial & Applied Mathematics (SIAM) [doi]
27. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RotDCF: Decomposition of convolutional filters for rotation-equivariant deep networks (May, 2019)  [abs]
28. Zhu, W; Qiu, Q; Calderbank, R; Sapiro, G; Cheng, X, Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters, Journal of Machine Learning Research, vol. 23 (January, 2022)  [abs]
29. Miao, Z; Wang, Z; Cheng, X; Qiu, Q, Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks, Advances in Neural Information Processing Systems, vol. 5 (January, 2021), pp. 3376-3388, ISBN 9781713845393  [abs]
30. Chen, Z; Li, Y; Cheng, X, SpecNet2: Orthogonalization-free Spectral Embedding by Neural Networks, Proceedings of Machine Learning Research, vol. 190 (January, 2022), pp. 287-302  [abs]
31. Cheng, X; Mishne, G, Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian., SIAM journal on imaging sciences, vol. 13 no. 2 (January, 2020), pp. 1015-1048 [doi]  [abs]
32. Tan, Y; Zhang, Y; Cheng, X; Zhou, X-H, Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions., Scientific reports, vol. 12 no. 1 (October, 2022), pp. 16630 [doi]  [abs]
33. Wang, Z; Cheng, X; Sapiro, G; Qiu, Q, STOCHASTIC CONDITIONAL GENERATIVE NETWORKS WITH BASIS DECOMPOSITION, 8th International Conference on Learning Representations, ICLR 2020 (January, 2020), OpenReview.net  [abs]
34. E, W; Zhou, X; Cheng, X, Subcritical bifurcation in spatially extended systems, Nonlinearity, vol. 25 no. 3 (March, 2012), pp. 761-779, IOP Publishing [doi]
35. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory, vol. 185 (April, 2018), pp. 48-64, Elsevier BV [doi]
36. CHENG, XIUYUAN; SINGER, AMIT, The Spectrum of Random Inner-product Kernel Matrices, Random Matrices: Theory and Applications, vol. 02 no. 04 (October, 2013), pp. 1350010-1350010, World Scientific Pub Co Pte Lt [doi]
37. Lee, J; Xie, Y; Cheng, X, Training Neural Networks for Sequential Change-Point Detection, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June (January, 2023), ISBN 9781728163277 [doi]  [abs]
38. Cheng, X; Cloninger, A; Coifman, RR, Two-sample statistics based on anisotropic kernels, Information and Inference: A Journal of the IMA (December, 2019), Oxford University Press (OUP) [doi]  [abs]
39. Chen, X; Cheng, X; Mallat, S, Unsupervised Deep Haar Scattering on Graphs., edited by Ghahramani, Z; Welling, M; Cortes, C; Lawrence, ND; Weinberger, KQ, Advances in Neural Information Processing Systems 27 (2014), pp. 1709-1717
40. Li, H; Lindenbaum, O; Cheng, X; Cloninger, A, Variational Diffusion Autoencoders with Random Walk Sampling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12368 LNCS (January, 2020), pp. 362-378, ISBN 9783030585914 [doi]  [abs]