arXiv research

New algorithm improves online binary classification with constant time complexity.

Federated learning is protected against adversarial attacks with residual-based reweighting.

Unified analysis of reweighted least-squares algorithms for linear models.

Improved matching for multiple objects using a novel reweighting method.

Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data. L1 PCA uses the L1 norm to measure error, whereas the conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the fitting error of…

Improved robustness in kernel-based regression via novel loss function and IRLS.

Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpose-built or off-the-shelf algorithms. We present an alternative approach to SVM fitting via the majorization--minimization (MM) paradigm. Alg…

Iteratively reweighted $\ell_1$ algorithm is a popular algorithm for solving a large class of optimization problems whose objective is the sum of a Lipschitz differentiable loss function and a possibly nonconvex sparsity inducing regularizer. In this paper, motivated by the success of extrapolation techniques in accele…

Kernel regression predicts graph signals in noisy environments.

Paper proves IRLS converges to subspace from any start, with practical benefits.

Simplifies neural regression by combining two sub-networks for predictions and uncertainties.

Global convergence for robust regression problems via IRLS with enhancements.

Method improves SINDy for noisy nonlinear systems.

Efficiently estimates rotations with corrupted data.

This work presents a general framework for solving the low rank and/or sparse matrix minimization problems, which may involve multiple non-smooth terms. The Iteratively Reweighted Least Squares (IRLS) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables an…

Iteratively reweighted least squares (IRLS) is a widely-used method in machine learning to estimate the parameters in the generalised linear models. In particular, IRLS for L1 minimisation under the linear model provides a closed-form solution in each step, which is a simple multiplication between the inverse of the we…

A novel AIRLS algorithm for multiaffine variable relations in high-dimensional problems.

New robust MPCA method handles casewise and cellwise outliers in tensor data.

A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. …

A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logis…

Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal correlation…

Paper proposes a method to learn cluster structured sparsity using adaptive reweighting.

Robust method estimates state, input, and parameters of linear systems online.

Paper develops robust regression method for heavy-tailed errors.

New convergence analysis for Lasso l1 reweighting improves practical performance.

We introduce a convex approach for mixed linear regression over $d$ features. This approach is a second-order cone program, based on L1 minimization, which assigns an estimate regression coefficient in $\mathbb{R}^{d}$ for each data point. These estimates can then be clustered using, for example, $k$-means. For problem…

The paper improves sample reweighting methods for adapting to covariate shifts.

A new framework detects changepoints in complex data.

Kernel methods can learn hierarchical polynomials efficiently.

Exact LAD line fitting via PALB with linear scaling and speed.

Many iterative and non-iterative methods have been developed for inverse problems associated with Ising models. Aiming to derive an accurate non-iterative method for the inverse problems, we employ the tree-reweighted approximation. Using the tree-reweighted approximation, we can optimize the rigorous lower bound of th…

Scalable method completes ill-conditioned matrices from few samples.

In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of …

Optimal Biweight kernel and computationally efficient Epanechnikov kernel for modal linear regression.

RFSVM with random features achieves faster learning rates.

This paper develops a novel deep recurrent neural network for sequential signal reconstruction.

Improved learning to reweight using deep interactions between student and teacher models.

Proposes a new robust expectile regression method for high-dimensional data.

In this paper we study general $l_p$ regularized unconstrained minimization problems. In particular, we derive lower bounds for nonzero entries of first- and second-order stationary points, and hence also of local minimizers of the $l_p$ minimization problems. We extend some existing iterative reweighted $l_1$ (IRL1) a…

RFM reduces feature space for linear models, improving sparse recovery.

Jointly learns neural networks across datasets to improve network quality.

New method improves stability of soft FQI for offline RL.

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…

A new method reduces the cost of solving large-scale linear models.

Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatia…

A new method for estimating large-scale linear models with improved precision.

Proposes a new sparse recovery method using generalized error function.

Recent work shows GRW approaches do not improve over ERM in distributional shift.