Search Results for author:
Found 13 papers, 4 papers with code
A cutting plane algorithm for globally solving low dimensional k-means clustering problems
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods.
Orthogonalization of data via Gromov-Wasserstein type feedback for clustering and visualization
In this paper we propose an adaptive approach for clustering and visualization of data by an orthogonalization process.
Quantifying and Computing Covariance Uncertainty
In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal.
On the complexity of the optimal transport problem with graph-structured cost
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals.
Mixed-Spectrum Signals -- Discrete Approximations and Variance Expressions for Covariance Estimates
Furthermore, we consider approximating signals with arbitrary spectral densities by sequences of singular spectrum, i. e., sinusoidal, processes, and derive the limiting behavior of covariance estimates as both the sample size and the number of sinusoidal components tend to infinity.
VidHarm: A Clip Based Dataset for Harmful Content Detection
Automatically identifying harmful content in video is an important task with a wide range of applications.
Incremental inference of collective graphical models
We consider incremental inference problems from aggregate data for collective dynamics.
Multi-marginal optimal transport and probabilistic graphical models
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.
Inference with Aggregate Data: An Optimal Transport Approach
Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP).
Automatic Target Recognition Using Discrimination Based on Optimal Transport
The use of distances based on optimal transportation has recently shown promise for discrimination of power spectra.
Data-driven nonsmooth optimization
In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function.
Optimization and Control 90C25 (Primary) 68T05, 47H05 (Secondary)
Learning to solve inverse problems using Wasserstein loss
We propose using the Wasserstein loss for training in inverse problems.
Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport
In particular we consider a limited-angle computerized tomography problem, where a priori information is used to compensate for missing measurements.
Optimization and Control 49N45, 90C25, 94A08
