2.1k citations · 2.4k across the 35 of their papers we have counts for
15 papers · 1 filter
Point Cloud Classification via Deep Set Linearized Optimal Transport
Scott Mahan, Caroline Moosmüller, Alexander Cloninger
We introduce Deep Set Linearized Optimal Transport, an algorithm designed for the efficient simultaneous embedding of point clouds into an space. This embedding preserves spe…
Semi-Supervised Laplace Learning on Stiefel Manifolds
Chester Holtz, Pengwen Chen, Alexander Cloninger +2
Motivated by the need to address the degeneracy of canonical Laplace learning algorithms in low label rates, we propose to reformulate graph-based semi-supervised learning as a non…
Effective resistance in metric spaces
Robi Bhattacharjee, Alexander Cloninger, Yoav Freund +1
Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigenvectors of the graph Laplacian. One attractive app…
Linearized Wasserstein dimensionality reduction with approximation guarantees
Alexander Cloninger, Keaton Hamm, Varun Khurana +1
We introduce LOT Wassmap, a computationally feasible algorithm to uncover low-dimensional structures in the Wasserstein space. The algorithm is motivated by the observation that ma…
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors
Chester Holtz, Gal Mishne, Alexander Cloninger
Probabilistic generative models provide a flexible and systematic framework for learning the underlying geometry of data. However, model selection in this setting is challenging, p…
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders
Stefan C. Schonsheck, Scott Mahan, Timo Klock +2
Autoencoding is a popular method in representation learning. Conventional autoencoders employ symmetric encoding-decoding procedures and a simple Euclidean latent space to detect h…
Structure from Voltage
Robi Bhattacharjee, Alex Cloninger, Yoav Freund +1
Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigen-vectors of the graph Laplacian. Graph laplacians…
StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm
Andreas Oslandsbotn, Zeljko Kereta, Valeriya Naumova +2
Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main m…
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features
Jinjie Zhang, Harish Kannan, Alexander Cloninger +1
We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the Random Fourier features (RFFs) associated with shift-invariant kernels. We…
A low discrepancy sequence on graphs
A. Cloninger, H. N. Mhaskar
Many applications such as election forecasting, environmental monitoring, health policy, and graph based machine learning require taking expectation of functions defined on the ver…
Cautious Active Clustering
Alexander Cloninger, Hrushikesh Mhaskar
We consider the problem of classification of points sampled from an unknown probability measure on a Euclidean space. We study the question of querying the class label at a very sm…
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado +2
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, im…
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger
In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We int…
Variational Diffusion Autoencoders with Random Walk Sampling
Henry Li, Ofir Lindenbaum, Xiuyuan Cheng +1
Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to app…
A witness function based construction of discriminative models using Hermite polynomials
H. N. Mhaskar, A. Cloninger, X. Cheng
In machine learning, we are given a dataset of the form , drawn as i.i.d. samples from an unknown probability distribution ; the marginal distrib…