arXiv research

Study explores geometric structure and prior for beta-logistic distribution.

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. …

New research shows logistic regression can achieve optimal error rate for agnostic learning of halfspaces.

Improved confidence bounds for linear logistic model with applications to bandits.

The paper extends logistic regression for unbounded majority classes and derives asymptotic properties.

Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logisti…

Improved logistic regression for robustness to distribution shifts.

This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains t…

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

New method estimates density ratio for well-separated distributions using multi-class logistic regression.

While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge. Classifiers may not behave as expected under certain ways of substituting the missing values, since they inherently make assumptions about the data distribution they were train…

Logistic regression for brain imaging without p-values.

Researchers derived formulas for joint moments of elliptical distributions.

Efficiently selects important variables in high-dimensional logistic regression.

Revises logistic-softmax likelihood for Bayesian meta-learning in few-shot classification.

Proposes logistic-beta process for modeling dependent probabilities with beta marginals.

Developed efficient distributed logistic regression for large datasets.

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…

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…

The paper calculates moments and conditional risks for skewed elliptical distributions.

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

Study develops a robust federated logistic regression for financial data analysis.

Flexible Bayesian approach for generalized linear models, especially for sparse logistic regression.

SLOE speeds up logistic regression in high dimensions with accurate signal strength estimation.

The paper proves asymptotic normality for multinomial logistic regression on null covariates.

A new method for Bayesian neural networks using probabilistic backpropagation.

Classifier chains are popular and effective method to tackle a multi-label classification problem. The aim of this paper is to study the asymptotic properties of the chain model in which the conditional probabilities are of the logistic form. In particular we find conditions on the number of labels and the distribution…

HiSS sampling overcomes local mode traps in rugged discrete spaces.

In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can e…

Large stepsizes can accelerate gradient descent for logistic regression.

New method distinguishes predictive distribution estimators in high-dimensional inputs.

This paper describes how to convert a machine learning problem into a series of map-reduce tasks. We study logistic regression algorithm. In logistic regression algorithm, it is assumed that samples are independent and each sample is assigned a probability. Parameters are obtained by maxmizing the product of all sample…

We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards. Using a fast inference procedure with Polya-Gamma distributed augmentation variables, we propose an improved version of Thomp…

The paper analyzes logistic regression for rare events data, deriving new insights on estimator efficiency and sampling strategies.

Early stopping improves neural networks' performance on binary classification tasks.

ACOWA improves distributed sparse classification with extra communication round.

Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging. This is primarily because one needs to compute the log-partition function on every data point. This makes distributing the computation hard. In this paper, we present a distributed stochastic gradient d…

New mechanism protects neural network weights from privacy attacks during self-supervised learning.

Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here,…

Online Platt Scaling adapts to varying data distributions.

A method for safe online classification reduces test costs while maintaining low error rates.

This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the exponential loss with the logistic and similar losses (specifically, twice differentiable convex losses which are Lipschitz and tend to zero on one side). The heart of the…

This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the technique of covariat…

New algorithms reduce regret in neural logistic bandits.

We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN…

Bayesian method estimates LTLL distribution parameters for time-to-event data.

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

Study high-dimensional logistic regression with missing data, providing exact error characterizations.