arXiv research

BELIEF framework interprets GLMs using binary linear models.

New algorithm learns sparse GLMs for binary outcomes efficiently.

BEGIN network models binary data without parametric assumptions.

Develops RF-GLS for binary geospatial data.

Paper introduces fair GLMs with convex penalty for equalizing GLM outcomes.

Sharp bounds on uniform generalization errors in binary linear classification.

The paper analyzes how overparameterized models can generalize well in multiclass classification.

Algorithm BGLM-OFU minimizes regret in combinatorial causal bandits with binary models.

Sharp bounds on binary model inference performance.

Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established…

Batch normalization biases linear models towards uniform margins, improving performance in binary classification.

Regularized linear regression improves binary classification performance, especially with ridge and $\ell_1$ regularization.

For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of feat…

Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure…

Spectral method speeds fitting of binary time series models.

New algorithm recovers sparse binary vectors from generalized linear measurements efficiently.

New method simplifies Bayesian analysis for categorical data.

diproperm tests differences in HDLSS data with binary classifiers.

Study shows fine-tuned linear models outperform pretrained ones in transfer learning.

ENTED efficiently decomposes binary and count tensors using nonparametric Gaussian processes.

Deep learning aids ADMM-based decoding for binary linear codes.

Grokking occurs in simple binary logistic classification near linear separability and noise.

The technological applications of hidden Markov models have been extremely diverse and successful, including natural language processing, gesture recognition, gene sequencing, and Kalman filtering of physical measurements. HMMs are highly non-linear statistical models, and just as linear models are amenable to linear a…

Our paper examines binary linear classification under Gaussian mixtures, revealing conditions for optimal performance.

This paper optimizes binary linear classifiers by tuning their weight vectors.

The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of the vector. Motivated by recent advances in compressive sensing with generative models, where a generative modeling assumption replaces the u…

Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through a fiber optic communication system. A highly accurate EDFA model is important because of its crucial role in optical network management and optimization. The input channels of…

Optimizes binary regression models with gradient ascent-descent methods.

We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering th…

Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. …

Multivariate binary data is becoming abundant in current biological research. Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relationships inside a multivariate binary data set by exploiting the underlying low rank structure. We re-expressed the logistic PCA model based on …

SMD outperforms SGD in over-parametrized linear models for certain data distributions.

New method for estimating gradients in stochastic binary networks.

We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification. We derive theoretical bounds on the true risk of SLIM scoring systems, and present experimental results to show that SLIM scoring systems are accurate, sparse, and interpretable classification models.

Study on recovering sparse linear classifiers from mixed binary responses.

Exact simulation of correlated binary outcomes using PMF constraints and linear programming.

New algorithm optimizes AUC in binary classification and changepoint detection.

Proposes a differentiable structure learning framework for general binary data.

Defines diversification as a binary relationship between financial portfolios.

A new method for binary ICA using non-stationary sources.

This paper optimizes retraining models using their own predictions and noisy labels.

Efficiently private regression for unbounded data.

The paper analyzes binary option markets with exogenous information and price sensitivity.

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

Unified framework for binary responses using AUC loss and low-rank constraint.

Enhances preference learning by incorporating response times into binary choices.

Gradient flow on ReLU networks converges to a simple model with few regions.

We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model's prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifol…