A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new screening rule improves lasso solving speed.
New screening rules improve lasso model fitting efficiency.
New rules reduce SLOPE model fitting time by screening out irrelevant variables.
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…
SRF learns sparse rule models by screening out features efficiently.
Introduces screening rules for non-convex Lasso problems.
A new screening rule 'dynamic Sasvi' improves sparse optimization speed.
Screening rules help identify active sets in optimization problems.
Safe screening rules reduce -regression computation by fixing 76% of variables.
A new screening rule speeds up OWL regression solving.
We propose a new framework for deriving screening rules for convex optimization problems. Our approach covers a large class of constrained and penalized optimization formulations, and works in two steps. First, given any approximate point, the structure of the objective function and the duality gap is used to gather in…
Safe screening rule reduces computational costs for Group OWL models.
Safe sample screening improves RSVM performance without sacrificing accuracy.
Safe screening rules reduce computation time in logistic regression with regularization.
Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed th…
A new screening rule improves SLOPE efficiency for high-dimensional data.
Safe screening rule improves Group SLOPE efficiency.
New screening rules speed up optimal design calculations.
In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity. Sparse-Group Lasso has recently been introduced in the context of l…
High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimization, hence speeding up solvers. When the procedure is proven not to discard features wrongly the rules are said to be \emph{safe}. In this …
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper…
We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet scree…
Screening rules allow to early discard irrelevant variables from the optimization in Lasso problems, or its derivatives, making solvers faster. In this paper, we propose new versions of the so-called for the Lasso. Based on duality gap considerations, our new rules create safe test regions whose d…
The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-scale problems. A nice property of SVM is that the non-support vectors have no effect on the resulting classifier. Motivated by …
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules propose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster s…
To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model…
The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently sc…
New method improves GLM optimization by leveraging dual iterates.
Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the number of high-order interaction features to be considered would be extremely large unless the number of covariates is sufficiently small. In thi…
Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of regression, this can be achiev…
A new method reduces feature size in CRFs for faster training.
Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates the features that are guaranteed to have zero coefficients for a certain value of the regularization parameter, is a technique for improving t…
Novel MCA-based rule mining speeds up psychiatric disorder screening.
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
Safe screening improves generalized CGM's feature selection stability.
The paper addresses fairness issues in screening classifiers, proposing within-group monotonicity to avoid unfair treatment of qualified candidates.
Paper introduces tCNNS model for predicting drug cell line interactions.
New algorithm handles large-scale interaction models efficiently.
Convex sparsity-promoting regularizations are ubiquitous in modern statistical learning. By construction, they yield solutions with few non-zero coefficients, which correspond to saturated constraints in the dual optimization formulation. Working set (WS) strategies are generic optimization techniques that consist in s…
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method a…
Trans-Ising combines auxiliary datasets to estimate high-dimensional Ising models.
Develops a continuous compliance index for Islamic equity screening.
High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametr…
The paper analyzes how employers can efficiently screen candidates using multiple tests, considering both skill estimation and fairness.
RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.
System detects financial news temporality combining NLP and ML.
RaSE screens variables via random subspaces, identifying joint effects.
This paper proposes a general adaptive procedure for budget-limited predictor design in high dimensions called two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS). SPARCS can be applied to high dimensional prediction problems in experimental science, medicine, finance, and engineer…