Publications of Xiuyuan Cheng    :chronological  alphabetical  combined  bibtex listing:

Papers Published

1. Calder, J; Cheng, X; Oberman, A; Ruthotto, L, PDEs for machine learning, Research in Mathematical Sciences, vol. 12 no. 3 (September, 2025) [doi]
2. Li, R; Qu, R; Parisi, F; Strino, F; Lam, H; Stanley, JS; Cheng, X; Myung, P; Kluger, Y, Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD)., Communications biology, vol. 8 no. 1 (July, 2025), pp. 1058 [doi]  [abs]
3. Qu, R; Cheng, X; Sefik, E; Stanley Iii, JS; Landa, B; Strino, F; Platt, S; Garritano, J; Odell, ID; Coifman, R; Flavell, RA; Myung, P; Kluger, Y, Gene trajectory inference for single-cell data by optimal transport metrics., Nature biotechnology, vol. 43 no. 2 (February, 2025), pp. 258-268 [doi]  [abs]
4. Xu, C; Cheng, X; Xie, Y, Computing high-dimensional optimal transport by flow neural networks, Proceedings of Machine Learning Research, vol. 258 (January, 2025), pp. 2872-2880  [abs]
5. Purohit, V; Repasky, M; Lu, J; Qiu, Q; Xie, Y; Cheng, X, Consistency Posterior Sampling for Diverse Image Synthesis, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (January, 2025), pp. 28327-28336 [doi]  [abs]
6. Cheng, X; Landa, B, Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise., Information and inference : a journal of the IMA, vol. 13 no. 4 (December, 2024), pp. iaae026 [doi]  [abs]
7. Cheng, X; Xie, Y, Kernel two-sample tests for manifold data, Bernoulli, vol. 30 no. 4 (November, 2024), pp. 2572-2597 [doi]  [abs]
8. Rosen, E; Hoyos, P; Cheng, X; Kileel, J; Shkolnisky, Y, The G -invariant graph Laplacian Part I: Convergence rate and eigendecomposition., Applied and computational harmonic analysis, vol. 71 (July, 2024), pp. 101637 [doi]  [abs]
9. Cheng, X; Lu, J; Tan, Y; Xie, Y, Convergence of Flow-Based Generative Models via Proximal Gradient Descent in Wasserstein Space, IEEE Transactions on Information Theory, vol. 70 no. 11 (January, 2024), pp. 8087-8106 [doi]  [abs]
10. Xu, C; Lee, J; Cheng, X; Xie, Y, Flow-Based Distributionally Robust Optimization, IEEE Journal on Selected Areas in Information Theory, vol. 5 (January, 2024), pp. 62-77 [doi]  [abs]
11. Repasky, M; Cheng, X; Xie, Y, STAGE-REGULARIZED NEURAL STEIN CRITICS FOR TESTING GOODNESS-OF-FIT OF GENERATIVE MODELS, ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (January, 2024), pp. 7255-7259 [doi]  [abs]
12. 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]
13. 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 (January, 2023), pp. 589-614, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
14. 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]
15. Rosen, E; Cheng, X; Shkolnisky, Y, The G-Invariant Graph Laplacian Part Ii: Diffusion Maps, in Elsevier BV, vol. 73 (2023), pp. 101695 [doi]  [abs]
16. 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]
17. 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]
18. 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]
19. 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]
20. 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]
21. 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]
22. 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]
23. 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]
24. 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]
25. 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]
26. 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]
27. 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]
28. 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]
29. 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]
30. 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]
31. 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]
32. 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]
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. 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]
35. 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]
36. 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]
37. Zhu, W; Qiu, Q; Calderbank, R; Sapiro, G; Cheng, X, Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters (September, 2019)  [abs]
38. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RotDCF: Decomposition of convolutional filters for rotation-equivariant deep networks (May, 2019)  [abs]
39. 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]
40. 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]
41. 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]
42. Yan, B; Sarkar, P; Cheng, X, Provable Estimation of the Number of Blocks in Block Models, Proceedings of Machine Learning Research, vol. 84 (January, 2018)  [abs]
43. Qiu, Q; Cheng, X; Calderbank, R; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters, Proceedings of Machine Learning Research, vol. 80 (January, 2018), pp. 4198-4207  [abs]
44. 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
45. 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]
46. 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]
47. 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]
48. 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]
49. 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
50. 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]
51. 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
52. 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]
53. 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]
54. 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]
55. 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]