arXiv research

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…

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

CD converges linearly for MCP/SCAD penalized least squares.

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…

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

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

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

Illustrates interleaved learning with Kalman Filter for linear least squares.

We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonst…

Cross validation residuals are well known for the ordinary least squares model. Here leave-M-out cross validation is extended to generalised least squares. The relationship between cross validation residuals and Cook's distance is demonstrated, in terms of an approximation to the difference in the generalised residual …

This paper optimizes sampling for least-squares approximation.

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…

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 …

Study improves least squares estimation for heavy-tailed errors.

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

Speeds up complex portfolio exposure calculations.

The paper examines prediction and estimation risks of ridgeless least squares under general error assumptions.

Improved Least-Squares Monte Carlo with finite-difference ansatz.

New method for option pricing using Monte Carlo and least squares.

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

New algorithm improves online binary classification with constant time complexity.

A fast sketching algorithm solves regularized least squares problems efficiently.

Paper uses deep learning to solve PDEs without supervision.

We study distributed learning with the least squares regularization scheme in a reproducing kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a data set into disjoint data subsets, applies the least squares regularization scheme to each data subset to produce an output function, an…

Least squares kernel based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottlene…

Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving l…

Proposes a partitioned least squares model for feature grouping.

Study identifies and validates a method for system identification of Markov jump linear systems.

Synthesizes robust estimators for domain adaptation.

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…

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

New algorithm reduces bias and variance in weighted least-squares solutions.

A new screening rule 'dynamic Sasvi' improves sparse optimization speed.

We establish adaptive results for trend filtering: least squares estimation with a penalty on the total variation of $(k-1)^{\rm th}$ order differences. Our approach is based on combining a general oracle inequality for the $\ell_1$-penalized least squares estimator with "interpolating vectors" to upper-bound the "effe…

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

The paper tackles robust reinforcement learning with performance guarantees.

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

Consider linear regression where the examples are generated by an unknown distribution on $R^d\times R$. Without any assumptions on the noise, the linear least squares solution for any i.i.d. sample will typically be biased w.r.t. the least squares optimum over the entire distribution. However, we show that if an i.i.d…

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…

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.

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…

This paper analyzes multi-pass SGD for least squares, improving generalization bounds.

This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm, converges to the true location parameter from a randomly initialized point. We establish the explicit convergence rates and sample complex…

Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off. In this paper, we investi…

Optimizes K inner simulations for least-square Monte Carlo to reduce computational cost.

This book introduces linear models and their theories rigorously.

Optimal weight windows are symmetric rectangles centered at peak.