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 12.3 (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 8.1 (July, 2025): 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 43.2 (February, 2025): 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 258 (January, 2025): 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): 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 13.4 (December, 2024): iaae026. [doi]  [abs]
7. Cheng, X; Xie, Y. "Kernel two-sample tests for manifold data." Bernoulli 30.4 (November, 2024): 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 71 (July, 2024): 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 70.11 (January, 2024): 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 5 (January, 2024): 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): 7255-7259. [doi]  [abs]
12. Repasky, M; Cheng, X; Xie, Y. "Neural Stein Critics with Staged L2-Regularization." IEEE Transactions on Information Theory 69.11 (November, 2023): 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 5.3 (January, 2023): 589-614. [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 2023-June (January, 2023). [doi]  [abs]
15. Rosen, E; Cheng, X; Shkolnisky, Y. "The G-Invariant Graph Laplacian Part Ii: Diffusion Maps." Elsevier BV 73 (2023): 101695. [doi]  [abs]
16. Cheng, X; Wu, N. "Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation." Applied and Computational Harmonic Analysis 61 (November, 2022): 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 12.1 (October, 2022): 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 68.10 (October, 2022): 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 11.3 (September, 2022): 889-957. [doi]  [abs]
20. Xu, C; Cheng, X; Xie, Y. "Invertible Neural Networks for Graph Prediction." IEEE Journal on Selected Areas in Information Theory 3.3 (September, 2022): 454-467. [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 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 190 (January, 2022): 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 118.22 (June, 2021): 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 130 (January, 2021): 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 5 (January, 2021): 3376-3388.  [abs]
27. Cheng, X; Xie, Y. "Neural Tangent Kernel Maximum Mean Discrepancy." Advances in Neural Information Processing Systems 9 (January, 2021): 6658-6670.  [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 28.5 (November, 2020): 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 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 12368 LNCS (January, 2020): 362-378. [doi]  [abs]
32. Cheng, X; Mishne, G. "Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian.." SIAM journal on imaging sciences 13.2 (January, 2020): 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).  [abs]
34. Alaifari, R; Cheng, X; Pierce, LB; Steinerberger, S. "On matrix rearrangement inequalities." Proceedings of the American Mathematical Society 148.5 (January, 2020): 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 107 (January, 2020): 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). [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 46.3 (May, 2019): 674-688. [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) 84 (April, 2018): 1185-1194.  [abs]
41. Cheng, X; Mishne, G; Steinerberger, S. "The geometry of nodal sets and outlier detection." Journal of Number Theory 185 (April, 2018): 48-64. [doi]
42. Yan, B; Sarkar, P; Cheng, X. "Provable Estimation of the Number of Blocks in Block Models." Proceedings of Machine Learning Research 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 80 (January, 2018): 4198-4207.  [abs]
44. Qiu, Q; Cheng, X; Calderbank, AR; Sapiro, G. "DCFNet: Deep Neural Network with Decomposed Convolutional Filters.." ICML 80 (2018): 4195-4204.
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 390.10112 (December, 2017): 2549-2558. [doi]
46. Pragier, G; Greenberg, I; Cheng, X; Shkolnisky, Y. "A Graph Partitioning Approach to Simultaneous Angular Reconstitution." IEEE Transactions on Computational Imaging 2.3 (September, 2016): 323-334. [doi]
47. Zhang, T; Cheng, X; Singer, A. "Marčenko–Pastur law for Tyler’s M-estimator." Journal of Multivariate Analysis 149 (July, 2016): 114-123. [doi]
48. Cheng, X; Chen, X; Mallat, S. "Deep Haar scattering networks." Information and Inference 5.2 (June, 2016): 105-133. [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 48 (June, 2016): 30-39.
50. Boumal, N; Cheng, X. "Concentration of the Kirchhoff index for Erdős–Rényi graphs." Systems & Control Letters 74 (December, 2014): 74-80. [doi]
51. Chen, X; Cheng, X; Mallat, S. "Unsupervised Deep Haar Scattering on Graphs.." Advances in Neural Information Processing Systems 27 (2014): 1709-1717.
52. CHENG, XIUYUAN; SINGER, AMIT. "The Spectrum of Random Inner-product Kernel Matrices." Random Matrices: Theory and Applications 02.04 (October, 2013): 1350010-1350010. [doi]
53. E, W; Zhou, X; Cheng, X. "Subcritical bifurcation in spatially extended systems." Nonlinearity 25.3 (March, 2012): 761-779. [doi]
54. Cheng, X; Lin, L; E, W; Zhang, P; Shi, A-C. "Nucleation of Ordered Phases in Block Copolymers." Physical Review Letters 104.14 (April, 2010). [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 229.5 (March, 2010): 1797-1809. [doi]