Xiuyuan Cheng

CoRR, 2024

Local Flow Matching Generative Models.

[BibT_eX]

[DOI]

CoRR, 2024

Posterior sampling via Langevin dynamics based on generative priors.

[BibT_eX]

[DOI]

CoRR, 2024

Training Guarantees of Neural Network Classification Two-Sample Tests by Kernel Analysis.

[BibT_eX]

[DOI]

Varun Khurana

CoRR, 2024

Stage-Regularized Neural Stein Critics For Testing Goodness-Of-Fit Of Generative Models.

[BibT_eX]

[DOI]

Matthew Repasky

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Neural Stein Critics With Staged L <sup>2</sup>-Regularization.

[BibT_eX]

[DOI]

Matthew Repasky

IEEE Trans. Inf. Theory, November, 2023

Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling.

[BibT_eX]

[DOI]

Boris Landa

SIAM J. Math. Data Sci., September, 2023

Deep graph kernel point processes.

[BibT_eX]

[DOI]

CoRR, 2023

G-invariant diffusion maps.

[BibT_eX]

[DOI]

Eitan Rosen

Yoel Shkolnisky

CoRR, 2023

Neural Differential Recurrent Neural Network with Adaptive Time Steps.

[BibT_eX]

[DOI]

Yixuan Tan

Liyan Xie

CoRR, 2023

Optimal transport flow and infinitesimal density ratio estimation.

[BibT_eX]

[DOI]

CoRR, 2023

The G-invariant graph Laplacian.

[BibT_eX]

[DOI]

Eitan Rosen

Yoel Shkolnisky

CoRR, 2023

Normalizing flow neural networks by JKO scheme.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Spatio-temporal point processes with deep non-stationary kernels.

[BibT_eX]

[DOI]

Zheng Dong

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Training Neural Networks for Sequential Change-Point Detection.

[BibT_eX]

[DOI]

Junghwan Lee

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Invertible Neural Networks for Graph Prediction.

[BibT_eX]

[DOI]

IEEE J. Sel. Areas Inf. Theory, September, 2022

Classification Logit Two-Sample Testing by Neural Networks for Differentiating Near Manifold Densities.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2022

Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Invertible normalizing flow neural networks by JKO scheme.

[BibT_eX]

[DOI]

CoRR, 2022

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise.

[BibT_eX]

[DOI]

Boris Landa

CoRR, 2022

Training neural networks using monotone variational inequality.

[BibT_eX]

[DOI]

CoRR, 2022

Police Text Analysis: Topic Modeling and Spatial Relative Density Estimation.

[BibT_eX]

[DOI]

Sarah Huestis-Mitchell

CoRR, 2022

SpecNet2: Orthogonalization-free Spectral Embedding by Neural Networks.

[BibT_eX]

[DOI]

Ziyu Chen

Yingzhou Li

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Neural Spectral Marked Point Processes.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Neural Spectral Marked Point Processes.

[BibT_eX]

[DOI]

CoRR, 2021

Kernel MMD Two-Sample Tests for Manifold Data.

[BibT_eX]

[DOI]

CoRR, 2021

Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation.

[BibT_eX]

[DOI]

Nan Wu

CoRR, 2021

Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Tangent Kernel Maximum Mean Discrepancy.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Graph Convolution with Low-rank Learnable Local Filters.

[BibT_eX]

[DOI]

Zichen Miao

Qiang Qiu

Proceedings of the 9th International Conference on Learning Representations, 2021

Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples.

[BibT_eX]

[DOI]

Yixing Zhang

Galen Reeves

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian.

[BibT_eX]

[DOI]

Gal Mishne

SIAM J. Imaging Sci., 2020

A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials.

[BibT_eX]

[DOI]

Hrushikesh N. Mhaskar

Frontiers Appl. Math. Stat., 2020

Convergence of Graph Laplacian with kNN Self-tuned Kernels.

[BibT_eX]

[DOI]

Hau-Tieng Wu

CoRR, 2020

ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution.

[BibT_eX]

[DOI]

CoRR, 2020

Graph Neural Networks with Low-rank Learnable Local Filters.

[BibT_eX]

[DOI]

Zichen Miao

Qiang Qiu

CoRR, 2020

A Dictionary Approach to Domain-Invariant Learning in Deep Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization.

[BibT_eX]

[DOI]

Zhongshu Xu

Yingzhou Li

Proceedings of Mathematical and Scientific Machine Learning, 2020

Stochastic Conditional Generative Networks with Basis Decomposition.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Variational Diffusion Autoencoders with Random Walk Sampling.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Classification Logit Two-sample Testing by Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Domain-invariant Learning using Adaptive Filter Decomposition.

[BibT_eX]

[DOI]

CoRR, 2019

Scale-Equivariant Neural Networks with Decomposed Convolutional Filters.

[BibT_eX]

[DOI]

CoRR, 2019

Diffusion Variational Autoencoders.

[BibT_eX]

[DOI]

CoRR, 2019

RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks.

[BibT_eX]

[DOI]

Yingzhou Li

Jianfeng Lu

CoRR, 2018

Defending against Adversarial Images using Basis Functions Transformations.

[BibT_eX]

[DOI]

CoRR, 2018

DCFNet: Deep Neural Network with Decomposed Convolutional Filters.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Provable Estimation of the Number of Blocks in Block Models.

[BibT_eX]

[DOI]

Bowei Yan

Purnamrita Sarkar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Two-sample Statistics Based on Anisotropic Kernels.

[BibT_eX]

[DOI]

Ronald R. Coifman

CoRR, 2017

2016

A Graph Partitioning Approach to Simultaneous Angular Reconstitution.

[BibT_eX]

[DOI]

IEEE Trans. Computational Imaging, 2016

Marčenko-Pastur law for Tyler's M-estimator.

[BibT_eX]

[DOI]

Teng Zhang

Amit Singer

J. Multivar. Anal., 2016

A Deep Learning Approach to Unsupervised Ensemble Learning.

[BibT_eX]

[DOI]

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Rotating MaxRS queries.

[BibT_eX]

[DOI]

Zitong Chen

Yubao Liu

Raymond Chi-Wing Wong

Jiamin Xiong

Peihuan Chen

Inf. Sci., 2015

Deep Haar Scattering Networks.

[BibT_eX]

[DOI]

Xu Chen

Stéphane Mallat

CoRR, 2015

2014

Concentration of the Kirchhoff index for Erdős-Rényi graphs.

[BibT_eX]

[DOI]

Nicolas Boumal

Syst. Control. Lett., 2014

Unsupervised Learning by Deep Scattering Contractions.

[BibT_eX]

[DOI]

Xu Chen

Stéphane Mallat

CoRR, 2014

Unsupervised Deep Haar Scattering on Graphs.

[BibT_eX]

[DOI]

Xu Chen

Stéphane Mallat

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Expected performance bounds for estimation on graphs from random relative measurements.

[BibT_eX]

[DOI]

Nicolas Boumal