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08/18/2023
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian
We investigate the concept of effective resistance in connection graphs,...
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07/31/2023
Semi-Supervised Laplacian Learning on Stiefel Manifolds
Motivated by the need to address the degeneracy of canonical Laplace lea...
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06/27/2023
Effective resistance in metric spaces
Effective resistance (ER) is an attractive way to interrogate the struct...
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03/21/2023
Non-degenerate Rigid Alignment in a Patch Framework
Given a set of overlapping local views (patches) of a dataset, we consid...
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02/14/2023
Linearized Wasserstein dimensionality reduction with approximation guarantees
We introduce LOT Wassmap, a computationally feasible algorithm to uncove...
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10/04/2022
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors
Probabilistic generative models provide a flexible and systematic framew...
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08/22/2022
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders
Autoencoding is a popular method in representation learning. Conventiona...
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01/25/2022
Supervised learning of sheared distributions using linearized optimal transport
In this paper we study supervised learning tasks on the space of probabi...
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09/29/2021
Kernel distance measures for time series, random fields and other structured data
This paper introduces kdiff, a novel kernel-based measure for estimating...
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08/23/2021
StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm
Kernel ridge regression (KRR) is a popular scheme for non-linear non-par...
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06/04/2021
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features
We propose the use of low bit-depth Sigma-Delta and distributed noise-sh...
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01/26/2021
LDLE: Low Distortion Local Eigenmaps
We present Low Distortion Local Eigenmaps (LDLE), a manifold learning te...
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09/18/2020
Natural Graph Wavelet Packet Dictionaries
We introduce a set of novel multiscale basis transforms for signals on g...
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08/20/2020
Linear Optimal Transport Embedding: Provable fast Wasserstein distance computation and classification for nonlinear problems
Discriminating between distributions is an important problem in a number...
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08/06/2020
ReLU nets adapt to intrinsic dimensionality beyond the target domain
We study the approximation of two-layer compositions f(x) = g(Ï•(x)) via ...
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08/03/2020
Cautious Active Clustering
We consider a set of points sampled from an unknown probability measure ...
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10/28/2019
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Stochastic-sampling-based Generative Neural Networks, such as Restricted...
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09/25/2019
Classification Logit Two-sample Testing by Neural Networks
The recent success of generative adversarial networks and variational le...
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06/03/2019
Data Sampling for Graph Based Unsupervised Learning: Convex and Greedy Optimization
In a number of situations, collecting a function value for every data po...
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12/11/2018
Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy
In this paper, we bound the error induced by using a weighted skeletoniz...
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04/25/2018
On the Dual Geometry of Laplacian Eigenfunctions
We discuss the geometry of Laplacian eigenfunctions -Δϕ = λϕ on compact ...
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12/14/2017
Outcome Based Matching
We propose a method to reduce variance in treatment effect estimates in ...
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09/14/2017
Two-sample Statistics Based on Anisotropic Kernels
The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) s...
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07/03/2017
People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs
We address the problem of defining a network graph on a large collection...
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10/31/2016
Function Driven Diffusion for Personalized Counterfactual Inference
We consider the problem of constructing diffusion operators high dimensi...
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07/15/2016
Spectral Echolocation via the Wave Embedding
Spectral embedding uses eigenfunctions of the discrete Laplacian on a we...
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06/02/2016
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
Medical practitioners use survival models to explore and understand the ...
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09/24/2015
Provable approximation properties for deep neural networks
We discuss approximation of functions using deep neural nets. Given a fu...
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07/01/2015
Bigeometric Organization of Deep Nets
In this paper, we build an organization of high-dimensional datasets tha...
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06/25/2015
