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Found 11 papers, 6 papers with code
On the Correctness of the Generalized Isotonic Recursive Partitioning Algorithm
This paper presents an in-depth analysis of the generalized isotonic recursive partitioning (GIRP) algorithm for fitting isotonic models under separable convex losses, proposed by Luss and Rosset [J. Comput.
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
In this paper, we explore the use of heavy-tailed models to combat over-regularization.
Wasserstein Geodesic Generator for Conditional Distributions
The conditional distributions given unobserved intermediate domains are on the Wasserstein geodesic between conditional distributions given two observed domain labels.
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert
In this paper, we conduct an in-depth analysis of the two modes of ISGD for smooth convex functions, namely proximal Robbins-Monro (proxRM) and proximal Poylak-Ruppert (proxPR) procedures, for their use in statistical inference on model parameters.
Proximity Operator of the Matrix Perspective Function and its Applications
We show that the matrix perspective function, which is jointly convex in the Cartesian product of a standard Euclidean vector space and a conformal space of symmetric matrices, has a proximity operator in an almost closed form.
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
We show that our approximation and risk consistency results naturally extend to the cases when data are locally perturbed.
High-Performance Statistical Computing in the Computing Environments of the 2020s
To our knowledge, this is the first demonstration of the feasibility of penalized regression of survival outcomes at this scale.
Computation
Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality
Through a close inspection of Ky Fan's classical result (1949) on the variational formulation of the sum of largest eigenvalues of a symmetric matrix, and a semidefinite programming (SDP) relaxation of the latter, we first provide a simple method to certify global optimality of a given stationary point of OTSM.
Optimization and Control Computation
Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET
Many imaging problems, such as total variation reconstruction of X-ray computed tomography (CT) and positron-emission tomography (PET), are solved via a convex optimization problem with near-circulant, but not actually circulant, linear systems.
Optimization and Control
Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation
Most recent research of neural networks in the field of computer vision has focused on improving accuracy of point predictions by developing various network architectures or learning algorithms.
General Classification
Ischemic Stroke Lesion Segmentation
+2
Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration
From this unification we propose a continuum of preconditioned forward-backward operator splitting algorithms amenable to parallel and distributed computing.
