arXiv research

New algorithm improves online binary classification with constant time complexity.

New methods improve accuracy and scalability for large datasets in multi-class classification.

Notes on linear classifiers and outliers, improving decision boundaries.

We introduce the implicitly constrained least squares (ICLS) classifier, a novel semi-supervised version of the least squares classifier. This classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semi-s…

Paper proposes a new method to improve classification performance over PCA.

Improves least squares classifiers with unlabeled data.

Reduced-rank method improves least-squares regression under output regularity.

We study randomized sketching methods for approximately solving least-squares problem with a general convex constraint. The quality of a least-squares approximation can be assessed in different ways: either in terms of the value of the quadratic objective function (cost approximation), or in terms of some distance meas…

We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semi-supervised method…

Unified multi-view learning framework using OPLS with regularization and deep extensions.

A new PMA method combines PCA and PLS for better classification.

A fast feature selection method using OLS and SOCC for classification.

This work proves convergence of adaptive resampling for random Fourier features.

Proposes a method to interpret linguistic data models using parse trees and least-squares scores.

CD converges linearly for MCP/SCAD penalized least squares.

ADMM-Softmax improves classification accuracy for multiclass problems.

The paper optimizes hyperplanes for binary classification in high-dimensional data with latent Gaussian mixtures.

Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functional regression, little attention has been paid to the understanding of their associated feature spaces. In this paper, we explore the potent…

LSGANs improve GANs by using least squares loss, leading to better image quality and stability.

Convolutional neural networks handle rotated image symmetries without dimensionality issues.

New method corrects least-squares temporal difference for better lambda-return estimation.

We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feat…

Cross validation residuals extended to GLS models.

Illustrates interleaved learning with Kalman Filter for linear least squares.

We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that …

Simplified proof shows SGD optimality for least squares.

The paper improves Kaczmarz algorithm with momentum for linear least squares.

Paper uses deep learning to solve PDEs without supervision.

New deep learning solver for high-dimensional derivative pricing.

New IRLS algorithms for SVM fitting via MM approach.

Proposes a partitioned least squares model for feature grouping.

A quantum-inspired classical algorithm speeds up LS-SVM classification.

ESNs trained with Tikhonov least squares approximate ergodic dynamical systems in L2(μ) norm.

Correcting bias in least squares regression with volume-rescaled sampling.

A new algorithm solves nonnegative least squares faster with nonnegative data.

The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that "kernelizes" the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted…

The paper identifies saddlepoints in unsupervised auto-encoding neural nets.

The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.

CNNs learn about input data uncertainties, improving classification performance.

Deep neural networks are optimal for dependent data using PAC-Bayes bounds.

Least Squares EM converges globally for log-concave mixtures.

The paper analyzes the risk of a least squares estimator under a spike covariance model.

New method speeds up solving L0-regularized least-squares problems.

Least squares estimator fails to achieve optimal risk in bounded distributions, but non-linear predictors can.

Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an $l_0$-constrained least-squares problem. The Orthogonal Least-Squares (OLS) algorithm sequentially selects the features (i.e., columns of the coefficient matrix) to greedil…

Study improves least squares estimation for heavy-tailed errors.

Stochastic Newton and quasi-Newton methods solve large linear least-squares problems efficiently.

We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Least Squares stands out of well-known classical approaches as e.g. Ridge Regression or Principal Components Regression, as it is not defined as…