arXiv research

Improves node classification in graphs with active learning.

We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformat…

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has be…

Improved logistic regression for robustness to distribution shifts.

A latent space model for a family of random graphs assigns real-valued vectors to nodes of the graph such that edge probabilities are determined by latent positions. Latent space models provide a natural statistical framework for graph visualizing and clustering. A latent space model of particular interest is the Rando…

Trans-GCR uses GCR model for node classification, providing theoretical guarantees and superior performance.

Bayesian meta learning improves uncertainty quantification in regression.

Paper finds a lower bound for estimating low-rank matrices in logistic regression.

Novel bounds for logistic regression coreset construction and feature selection.

Unified framework for sparse logistic regression with nonconvex regularization.

Prevalidated ridge regression simplifies logistic regression for high-dimensional data.

Improved sketching for logistic and $\ell_1$ regression with near-linear dimensions.

Safe screening rules reduce computation time in logistic regression with $\ell_0-\ell_2$ regularization.

Improves logistic regression performance with nonconvex programming.

Paper explores connections between loss functions and consistency in binary classification and regression.

Maximum likelihood estimator performance in logistic regression analyzed.

We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.

Paper introduces imprecise logistic regression for handling uncertain data.

A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.

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…

PIANO speeds up multinomial logistic regression solving.

Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logis…

A new algorithm approximates logistic regression probabilities efficiently.

Unified approach learns Ising models from various dynamics and data types.

Study shows sample complexity for logistic regression with normal covariates.

Logistic regression gets a new, simpler uniform bound.

We consider a wide range of regularized stochastic minimization problems with two regularization terms, one of which is composed with a linear function. This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, a…

Proposes MELODIC family for simultaneous binary logistic regression.

Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. …

Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable…

Improved classifier for PU data using logistic regression.

We characterize the effectiveness of a classical algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples. The algorithm is (appropriately regularized) maximum conditional log-likelihood, which involves solving a convex program for each node; for Ising models this is …

Disagreement between two classifiers regarding the class membership of an observation in pattern recognition can be indicative of an anomaly and its nuance. As in general classifiers base their decision on class aposteriori probabilities, the most natural approach to detecting classifier incongruence is to use divergen…

A neural network solves logistic regression with $\ell_1$ regularization efficiently.

Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hid…

Improved logistic regression for multi-omics data improves prediction and variable selection.

In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost…

Improved multiclass logistic regression with lower computational complexity.

Paper proposes a new method for estimating conditional densities using logistic regressions.

Decision trees and logistic regression are one of the most popular and well-known machine learning algorithms, frequently used to solve a variety of real-world problems. Stability of learning algorithms is a powerful tool to analyze their performance and sensitivity and subsequently allow researchers to draw reliable c…

Machine learning improves joint default assessment by capturing non-linear dependencies.

Early stopping improves logistic regression's calibration and consistency in high dimensions.

New method tackles parcel routing with AI.

We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs th…

Large stepsizes can accelerate gradient descent for logistic regression.

Paper introduces data-dependent SSP for private linear and logistic regression.

A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each. This paper observes that if the inference problem is "smoothed" through the addition of entropy terms, for fixed messages, the learning ob…

Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically, we introduce sparsity inducing penalties over parameter differenc…