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Preprints:

  1. Y. Xie and X. Cheng. "Flow-based generative models as iterative algorithms in probability space." (Tutorial paper) [arXiv: 2502.13394]
  2. X. Cheng, T. Gong, Y. Xie. "Point processes with event time uncertainty." [arXiv: 2411.02694]
  3. C. Xu, X. Cheng, Y. Xie. "Local Flow Matching generative models." [arXiv: 2410.02548]
  4. V. Purohit, M. Repasky, J. Lu, Q. Qiu, Y. Xie, X. Cheng. "Posterior sampling via Langevin dynamics based on generative priors." [arXiv: 2410.02078]
  5. T. Tang, N. Wu, X. Cheng, D. Dunson. "Adaptive Bayesian regression on data with low intrinsic dimensionality." [arXiv: 2407.09286]
  6. V. Khurana, X. Cheng, A. Cloninger. “Training guarantees of neural network classification two-sample tests by kernel analysis.” [arXiv: 2407.04806]
  7. Z. Dong, M. Repasky, X. Cheng, and Y. Xie. “Deep graph kernel point processes.” [arXiv: 2306.11313]
  8. Y. Tan, L. Xie, and X. Cheng. “Neural differential Recurrent Neural Network with adaptive time steps.” [arXiv: 2306.01674]
  9. C. Xu, X. Cheng, and Y. Xie. "An alternative approach to train neural networks using monotone variational inequality." [arXiv: 2202.08876] [Code]
  10. J. Zhou, S. Huestis-Mitchell, X. Cheng, Y. Xie. “Crime hot-spot modeling via topic modeling and relative density estimation.” [arXiv: 2202.04176]
  11. Z. Wang, X. Cheng, G. Sapiro, and Q. Qiu. “ACDC: Weight sharing in atom-coefficient decomposed convolution.” [arXiv: 2009.02386]
  12. U. Shaham, J. Garritano, Y. Yamada, E. Weinberger, A. Cloninger, X. Cheng, K. Stanton, and Y. Kluger. “Defending against adversarial images using basis functions transformations.” [arXiv: 1803.10840]

Journal publications:
  1. X. Cheng and B. Landa. “Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise." Information and Inference: A Journal of the IMA (2024). [Abstract] [arXiv: 2206.11386] [Code]
  2. E. Rosen, X. Cheng, Y. Shkolnisky. "The G-invariant graph Laplacian part II: Diffusion maps." Applied and Computational Harmonic Analysis (2024). [Abstract] [arXiv: 2306.07350]
  3. X. Cheng, J. Lu, Y. Tan, Y. Xie. “Convergence of flow-based generative models via proximal gradient descent in Wasserstein space.” IEEE Transactions on Information Theory (2024). [Abstract] [arXiv: 2310.17582] [Code]
  4. X. Cheng and Y. Xie. “Kernel two-sample tests for manifold data.” Bernoulli Journal (2024). [Abstract] [arXiv: 2105.03425] [Code]
  5. R. Qu, X. Cheng, E. Sefik, J.S. Stanley, B. Landa, F. Strino, S. Platt, J. Garritano, I. Odell, R. Coifman, R.A. Flavell, P. Myung, Y. Kluger. "Gene trajectory inference for single-cell data by optimal transport metrics." Nature Biotechnology (2024). [Abstract] [bioRxiv: 2022.07.08.499404v3] [Code]
  6. E. Rosen, P. Hoyos, X. Cheng, J. Kileel, Y. Shkolnisky. "The G-invariant graph Laplacian Part I: Convergence rate and eigendecomposition." Applied and Computational Harmonic Analysis (2024). [Abstract] [arXiv: 2303.17001] Original name: “The G-invariant graph Laplacian”.
  7. C. Xu, J. Lee, X. Cheng, Y. Xie. "Flow-based Distributionally Robust Optimization." IEEE Journal on Selected Areas in Information Theory (JSAIT) (2024). [Abstract] [arXiv: 2310.19253] [Code]
  8. M. Repasky, X. Cheng, and Y Xie. “Neural Stein critics with staged L2-regularization.” IEEE Transactions on Information Theory (2023). [Abstract] [arXiv: 2207.03406] [Code]. Short version: "Stage-regularized neural Stein critics for testing Goodness-of-fit of generative models" (ICASSP 2024). [Abstract]
  9. B. Landa and X. Cheng. “Robust inference of manifold density and geometry by doubly stochastic scaling.” SIAM Journal on Mathematics of Data Science (SIMODS) (2023). [Abstract] [arXiv: 2209.08004]
  10. C. Xu, X. Cheng, and Y. Xie. “Invertible neural networks for graph prediction.” IEEE Journal on Selected Areas in Information Theory (JSAIT) (2022). [Abstract] [arXiv: 2206.01163] [Code]
  11. Y. Tan, Y. Zhang, X. Cheng, and X.-H. Zhou. "Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions". Scientific Reports (2022). [Abstract] [medRxiv: 2022.01.01.21268139] [Code]
  12. X. Cheng and N. Wu. "Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation". Applied and Computational Harmonic Analysis, 61, 132-190 (2022). [Abstract] [arXiv: 2101.09875] [Code]
  13. X. Cheng and A. Cloninger. "Classification logit two-sample testing by neural networks for differentiating near manifold densities.'' IEEE Transactions on Information Theory (2022). [Abstract] [arXiv: 1909.11298] [Code] Original name: "Classification logit two-sample testing by neural networks".
  14. W. Zhu, Q. Qiu, R. Calderbank, G. Sapiro, and X. Cheng. "Scaling-translation-equivariant networks with decomposed convolutional filters". Journal of Machine Learning Research (2022). [Abstract] [arXiv: 1909.11193]
  15. X. Cheng and H.-T. Wu. "Convergence of graph Laplacian with kNN self-tuned kernels". Information and Inference: A Journal of the IMA (2021). [Abstract] [arXiv: 2011.01479]
  16. J. Zhao, A. Jaffe, H. Li, O. Lindenbaum, E. Sefik, R. Jackson, X. Cheng, R. Flavell, and Y. Kluger. "Detection of differentially abundant cell subpopulations in scRNA-seq data". Proceedings of the National Academy of Sciences 118, no. 22 (2021). [Abstract] [bioRxiv: 10.1101/711929v3]
  17. Y. Li, X. Cheng, and J. Lu. "Butterfly-net: optimal function representation based on convolutional neural networks". Communications in Computational Physics (2020). [Abstract] [arXiv: 1805.07451]
  18. H. N. Mhaskar, A. Cloninger, and X. Cheng. "A witness function based construction of discriminative models using Hermite polynomials". Frontiers in Applied Mathematics and Statistics, section Mathematics of Computation and Data Science (2020). [Abstract] [arXiv: 1901.02975]
  19. X. Cheng and G. Mishne. "Spectral embedding norm: looking deep into the spectrum of the graph Laplacian". SIAM Journal on Imaging Sciences (2020). [Abstract] [arXiv: 1810.10695] [Code]
  20. R. Alaifari, X. Cheng, L. B. Pierce, and S. Steinerberger. "On matrix rearrangement inequalities". Proceedings of the AMS (2020). [Abstract] [arXiv: 1904.05239]
  21. X. Cheng, A. Cloninger, and R. R. Coifman. "Two-sample statistics based on anisotropic kernels". Information and Inference: A Journal of the IMA (2019). [Abstract] [arXiv:1709.05006]
  22. X. Cheng, M. Rachh, and S. Steinerberger. "On the diffusion geometry of graph Laplacians and applications". Applied and Computational Harmonic Analysis , 46(3), 674-688 (2019). [Abstract] [arXiv:1611.03033]
  23. X. Cheng, G. Mishne, and S. Steinerberger. "The geometry of nodal sets and outlier detection". Journal of Number Theory (2017). [Abstract] [arXiv:1706.01362]
  24. J. Lu, Y. Lu, X. Wang, X. Li, G.C. Linderman, C. Wu, X. Cheng, L. Mu, H. Zhang, J. Liu, M. Su, H. Zhao, E.S. Spatz, J.A. Spertus, F.A. Masoudi, H.M. Krumholz, and L. Jiang. "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), 2549-2558 (2017). [Abstract]
  25. X. Cheng, X. Chen, and S. Mallat. "Deep Haar scattering networks". Information and Inference: A Journal of the IMA (2016). [Abstract] [PDF]
  26. G. Pragier, I. Greenberg, X. Cheng, and Y. Shkolnisky. "A graph partitioning approach to simultaneous angular reconstitution". IEEE Transactions on Computational Imaging (2016). [Abstract] [PDF]
  27. T. Zhang, X. Cheng, and A. Singer. "Marchenko-Pastur law for Tyler's M-estimators". Journal of Multivariate Analysis, 149, 114-123 (2016). [Abstract] [PDF]
  28. N. Boumal and X. Cheng. "Concentration of the Kirchhoff index for Erdos-Rényi graphs". System and Control Letters, 74, 74-80 (2014). [Abstract] [PDF]
  29. X. Cheng and A. Singer. "The spectrum of high-dimensional random inner-product matrices". Random Matrices: Theory and Applications, 02, 04 (2013). [Abstract] [PDF]
  30. W. E, X. Zhou, and X. Cheng. "Subcritical bifurcation in spatially extended systems". Nonlinearity, 25, 761 (2012). [PDF]
  31. X. Cheng, L. Lin, W. E, P. Zhang, and A.C. Shi. "Nucleation of ordered phases in block copolymers". Physical Review Letters, 104, 148301 (2010). [PDF]
  32. L. Lin, X. Cheng, W. E, A.-C. Shi, and P. Zhang. "A numerical method for the study of nucleation of ordered phases". Journal of Computational Physics, 229, 1797 (2010). [PDF]

Conference publications:

  1. C. Xu, X. Cheng, and Y. Xie. “Computing high-dimensional optimal transport by flow neural networks.” The 28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025). [Abstract] [arXiv: 2305.11857] [Code]
  2. C. Xu, X. Cheng, and Y. Xie. “Normalizing flow neural networks by JKO scheme.” NeurIPS 2023 (spotlight). [Abstract] [arXiv: 2212.14424] [Code]
  3. Z. Dong, X. Cheng, and Y. Xie. “Spatio-temporal point processes with deep non-stationary kernels.” The 11th International Conference on Learning Representations (ICLR 2023). [Abstract] [arXiv: 2211.11179]
  4. J. Lee, Y. Xie, and X. Cheng. “Training neural networks for sequential change-point detection.” 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). [Abstract] [arXiv: 2210.17312]
  5. Z. Chen, Y. Li, and X. Cheng. "SpecNet2: Orthogonalization-free spectral embedding by neural networks." The Third Mathematical and Scientific Machine Learning Conference (MSML 2022). [Abstract] [arXiv: 2206.06644] [Code]
  6. S. Zhu, H. Wang, Z. Dong, X. Cheng, and Y. Xie. "Neural spectral marked point processes". The 10th International Conference on Learning Representations (ICLR 2022). [Abstract] [arXiv: 2106.10773] [Code]
  7. X. Cheng and Y. Xie. "Neural tangent kernel maximum mean discrepancy". The 35th Conference on Neural Information Processing Systems (NeurIPS 2021). [Abstract] [arXiv: 2106.03227] [Code]
  8. Z. Miao, Z. Wang, X. Cheng, and Q. Qiu. "Spatiotemporal joint filter decomposition in 3D convolutional neural networks". The 35th Conference on Neural Information Processing Systems (NeurIPS 2021). [Abstract]
  9. Y. Zhang, X. Cheng, G. Reeves. "Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples". The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021). [Abstract] [arXiv: 2103.00394]
  10. X. Cheng, Z. Miao, and Q. Qiu. "Graph convolution with low-rank learnable local filters". International Conference on Learning Representations (ICLR 2021) (spotlight). [Abstract] [arXiv: 2008.01818v2] [Code]
  11. Z. Wang, X. Cheng, G. Sapiro, and Q. Qiu. "A dictionary approach to domain-invariant learning in deep networks". 34th Conference on Neural Information Processing Systems (NeurIPS 2020). [Abstract] [arXiv: 1909.11285] Original name: "Domain-invariant learning using adaptive filter decomposition".
  12. H. Li, O. Lindenbaum, X. Cheng, and A. Cloninger. "Variational diffusion autoencoders with random walk sampling". 2020 European Conference on Computer Vision (ECCV 2020). [Abstract] [arXiv: 1905.12724]
  13. Z. Wang, X. Cheng, G. Sapiro, and Q. Qiu. "Stochastic conditional generative networks with basis decomposition." International Conference on Learning Representations (ICLR 2020). [Abstract] [arXiv: 1909.11286]
  14. Z. Xu, Y. Li, and X. Cheng. "Butterfly-net2: simplified Butterfly-net and Fourier transform initialization." The First Mathematical and Scientific Machine Learning Conference (MSML 2020). [Abstract] [arXiv: 1912.04154] [Code]
  15. X. Cheng, Q. Qiu, R. Calderbank, and G. Sapiro. "RotDCF: Decomposition of convolutional filters for rotation-equivariant deep networks". International Conference on Learning Representations (ICLR 2019). [Abstract] [arXiv: 1805.06846] [Code] (Pytorch code by Zichen Miao)
  16. Q. Qiu, X. Cheng, R. Calderbank, and G. Sapiro. "DCFNet: Deep neural network with decomposed convolutional filters”. Proceedings of The 35rd International Conference on Machine Learning (ICML 2018). [Abstract] [arXiv:1802.04145] [Code: Matlab, Pytorch (by Ze Wang)]
  17. B. Yan, P. Sarkar, and X. Cheng. "Provable estimation of the number of blocks in block models". Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2018). [Abstract] [arXiv:1705.08580]
  18. U. Shaham, X. Cheng, O. Dror, A. Jaffe, B. Nadler, J. Chang and Y. Kluger. "A deep learning approach to unsupervised ensemble learning". Proceedings of The 33rd International Conference on Machine Learning (ICML 2016). [Abstract] [PDF]
  19. X. Chen, X. Cheng, and S. Mallat. "Unsupervised deep Haar scattering on graphs". Advances in Neural Information Processing Systems 27 (NIPS 2014). [Abstract] [Explanation] [Code] [PDF]