arXiv research

BELIEF framework interprets GLMs using binary linear 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…

BEGIN network models binary data without parametric assumptions.

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

Spectral method speeds fitting of binary time series models.

New algorithm learns sparse GLMs for binary outcomes efficiently.

Develops RF-GLS for binary geospatial data.

diproperm tests differences in HDLSS data with binary classifiers.

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

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

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

Sharp bounds on uniform generalization errors in binary linear classification.

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

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

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

Optimizes binary regression models with gradient ascent-descent methods.

New method simplifies Bayesian analysis for categorical data.

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…

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.

New algorithm optimizes AUC in binary classification and changepoint detection.

Defines diversification as a binary relationship between financial portfolios.

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

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…

Efficiently private regression for unbounded data.

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…

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

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

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

Enhances preference learning by incorporating response times into binary choices.

Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom eva…

Efficiently poisons offline RLHF models by flipping preference labels.

Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in social networks. If one-vs-all methods usually reach top performance in this context, these approaches suffer from a high inference complexity…

New test for SGD in binary classification reduces computation time.

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 …

Binary testing for softmax models requires many samples, similar to leverage score models.

Quantum machine learning model for binary classification.

A new method for LDA using randomized Kaczmarz improves accuracy for large datasets.

We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bit…

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

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…

Paper improves DP-ERM for binary linear classification with large-margin subsets.

In this article, we consider a 2 factors-model for pricing defaultable bond with discrete default intensity and barrier where the 2 factors are stochastic risk free short rate process and firm value process. We assume that the default event occurs in an expected manner when the firm value reaches a given default barrie…

Paper proposes GEG to enhance fairness in binary and multi-class classification.

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

Linear classifiers can resist adversarial attacks on Gaussian data.

Study on recovering sparse linear classifiers from mixed binary responses.