The logistic equation describes wealth condensation in a WAA-enhanced asset exchange model.
problem Analyzing wealth condensation in asset exchange models with wealth advantage.
method Introduced a continuous wealth advantage bias in the YSM, derived a logistic equation for the condensed wealth.
result Condensation of wealth follows a logistic equation in time.
We study the steady state solutions of a generalized logistic type equation on a complete Riemannian manifold. We provide sufficient conditions for existence, respectively non-existence of positive solutions, which depend on the relative size of the coefficients and their mutual interaction with the geometry of the man…
Proposes MELODIC family for simultaneous binary logistic regression.
problem Simultaneous analysis of multiple binary response variables.
method Defines models in a reduced Euclidean space using a distance rule.
result Improves predictive accuracy through model interdependence.
Regularization improves logistic regression performance in high-dimensional settings.
problem Improving logistic regression in scenarios with many parameters and observations.
method Introducing a convex regularizer to the negative log-likelihood function to encourage desired structures.
result Explicit expressions for various performance metrics of regularized logistic regression are derived.
Hybrid machine learning improves gallstone risk prediction.
problem Complex gallstone disease risk factors and interactions.
method Adaptive LASSO for variable selection, BART for interactions, differential equations for interpretation.
result Enhanced prediction accuracy and actionable insights.
The time development of the price of a financial asset is considered by constructing and solving Langevin equations for a homogeneously saturated model, and for comparison, for a standard model and for a logistic model. The homogeneously saturated model uses coupled rate equations for the money supply and for the price…
A new training method uses multilevel minimization for machine learning.
problem Training machine learning models with high variance and low efficiency.
method Constructs a multilevel hierarchy by reducing sample size and internally trains surrogate models with fewer samples.
result The multilevel method enhances model training efficiency compared to subsampled Newton's and variance reduction methods.
Paper proves convergence of multi-class logistic regression network.
problem Ensuring convergence of multi-class logistic regression network.
method Mathematical derivation of gradient and second derivative expressions.
result Gradient descent can be used without L2-regularizer to guarantee convergence. Paper analyzes GMM for separable data with various parameter structures.
problem Classifying separable data with logistic models and their generalizations.
method Introduces and analyzes Generalized Margin Maximizer (GMM) for logistic models with specific parameter structures.
result GMM outperforms max-margin classifiers in various parameter settings and structures.
The paper establishes convergence rates for MoE models in classification problems.
problem Understanding the behavior of MoE models in classification settings.
method Established convergence rates for density and parameter estimation in softmax gating multinomial logistic MoE models.
result Parameter estimation rates are significantly improved with a novel modified softmax gating function.
Logistic regression connects to perceptron learning via gradient ascent.
problem No specific problem stated; focuses on connection between algorithms.
method Gradient ascent for logistic regression compared to perceptron learning.
result Gradient ascent for logistic regression is a soft variant of perceptron learning.
The likelihood ratio test in high-dimensional logistic regression is not a chi-square, but a rescaled one.
problem The incorrect chi-square approximation in high-dimensional logistic regression.
method Proving the rescaled chi-square distribution and solving nonlinear equations.
result The likelihood ratio test is a rescaled chi-square, not a standard chi-square.
Maximum likelihood estimator performance in logistic regression analyzed.
problem Performance of maximum likelihood estimator in logistic regression.
method Sharp non-asymptotic guarantees for existence and excess logistic risk.
result Sharp guarantees for the existence and excess risk of MLE in logistic regression.
Examines WENDy-IRLS algorithm's noise robustness and efficiency in various differential equations.
problem Noise robustness and efficiency of WENDy-IRLS algorithm.
method Studied coverage and bias properties of WENDy-IRLS algorithm's estimators in various differential equations and noise distributions.
result WENDy-IRLS algorithm shows notable noise robustness and computational efficiency.
Paper finds a lower bound for estimating low-rank matrices in logistic regression.
problem Estimating low-rank coefficient matrices in logistic regression.
method Derives a minimax lower bound on the risk.
result The bound depends on matrix dimensions, rank, and sample size.
Revises logistic-softmax likelihood for Bayesian meta-learning in few-shot classification.
problem Inherent uncertainty in logistic-softmax leads to suboptimal performance in meta-learning.
method Redesigns logistic-softmax likelihood with a temperature parameter for better control of prior confidence.
result Achieves well-calibrated uncertainty estimates and comparable/superior performance on benchmark datasets.
Logitron combines Perceptron and logistic loss for improved classification.
problem Non-convex and non-smooth zero-one loss function in classification models.
method Introduces a Perceptron-augmented convex classification framework with an extended logistic loss function.
result Hinge-Logitron outperforms logistic regression and SVM in classification accuracy.
Novel bounds for logistic regression coreset construction and feature selection.
problem Efficiently summarize and reduce logistic regression inputs.
method Feature space sketching for logistic regression.
result Tight bounds for coreset construction and feature selection.
Unified framework for sparse logistic regression with nonconvex regularization.
problem Sparse logistic regression with nonconvex regularization.
method Unified framework, line search criteria for nonconvex terms.
result Effective classification and feature selection at lower computational cost.
Study explores geometric structure and prior for beta-logistic distribution.
problem Understanding the geometric structure and prior distributions of the beta-logistic distribution.
method Exploring dual geometric structure and uncovering α-parallel prior. result The beta-logistic distribution admits an α-parallel prior for any real number α. Improves logistic regression performance on imbalanced data.
problem Imbalanced data leads to all labels being estimated as majority class.
method Uses F-measure optimization to estimate relative density ratio and approximate relative F-measure.
result Proposed method improves logistic regression performance on imbalanced data.
Predictive models of training load data failed to accurately predict injuries in Australian football.
problem Predicting injuries in Australian football using training load data.
method Training load data from GPS, accelerometers, and player ratings were analyzed using various predictive models.
result The best model for hamstring injuries had an AUC of 0.76, but overall predictive performance was poor.
Research uses Twitter data to analyze public perception of city logistics.
problem Understanding public views on city logistics from multiple stakeholders.
method Collecting Twitter content, applying unsupervised learning and NLP.
result Built an Interest Map and determined sentiment of city logistics entries.
Graph-Sparse Logistic Regression for sparse and connected support classification.
problem Sparse and connected support classification problems.
method Introduces Graph-Sparse Logistic Regression algorithm.
result Validated and benchmarked against L1-regularized Logistic Regression.
Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.
problem Understanding and optimizing loss functions for deep neural networks with class imbalances.
method Analyzing necessary conditions for convergence of logistic and softmax losses in CNNs.
result Proposes a novel reweighted logistic loss function that improves performance over softmax loss.
New method creates coresets for logistic regression, even for hard data.
problem Creating efficient coresets for logistic regression analysis.
method Introduce a complexity measure μ(X) and a sensitivity sampling scheme for bounded μ(X) complexity.
result First provably sublinear (1±ε)-coreset for logistic regression.
Deep neural networks classify chaotic time series.
problem Classifying chaotic time series with high accuracy.
method Train neural networks on simpler systems to classify more complex ones.
result Convolutional neural networks outperform other networks for time series classification.
Improved sketching for logistic and ℓ1 regression with near-linear dimensions.
problem Efficiently approximate ℓ1 and logistic regression problems. method New sketching techniques achieving near-linear dimensions for both problems.
result Achieved near-linear sketching dimensions for ℓ1 and logistic regression. Paper introduces MLRH, a probabilistic model for multilabel classification.
problem Multilabel classification challenges in various domains.
method Introduces hidden variables to relax one-hot-encoding in logistic regression.
result Probabilistic model achieves competitive performance compared to other algorithms.
The logistic network Lasso solves binary classification and clustering for network data.
problem Binary classification and clustering for network-structured data.
method Generalizes logistic regression to non-Euclidean network data, uses total variation regularization, and applies ADMM for scalability.
result Solves non-smooth convex regularized empirical risk minimization with logistic loss.
Safe screening rules reduce computation time in logistic regression with ℓ0−ℓ2 regularization.
problem Efficiently solving logistic regression with many features and regularization.
method Screening rules based on Fenchel dual lower bounds of strong conic relaxations.
result A high percentage of features can be safely removed before solving, leading to substantial speed-up.
Word2vec skip-gram with negative sampling is explained as a weighted logistic PCA.
problem Understanding word2vec skip-gram with negative sampling.
method Equivalence to weighted logistic PCA, comparison to other methods, extension to higher dimensions.
result Word2vec skip-gram with negative sampling is a weighted logistic PCA.
Study uses machine learning, linear, and Bayesian models for logistic regression in manufacturing failures detection.
problem Manufacturing failures detection using logistic regression models.
method Machine learning (XGBoost), linear, and Bayesian approaches for logistic regression.
result Bayesian approach provides statistical distribution for model parameters, useful for probabilistic analysis.
Proposes logistic-beta process for modeling dependent probabilities with beta marginals.
problem Limited work on flexible and computationally convenient stochastic process extensions for dependent random probabilities.
method Introduces logistic-beta process with logistic transformation and beta marginals, capable of modeling dependence in discrete and continuous domains.
result Logistic-beta processes enable effective posterior inference and design of computationally tractable dependent Bayesian nonparametric models.
Improved regret bounds for logistic bandits via novel confidence set construction.
problem Dependencies in parameter space for logistic bandits, especially when S≥d. method Regret-to-confidence-set conversion (R2CS) to construct convex confidence sets.
result Strict improvement in regret bound w.r.t. S in logistic bandits. Paper introduces imprecise logistic regression for handling uncertain data.
problem Uncertainties in data prevent traditional logistic regression from being applied effectively.
method Develops imprecise logistic regression model using intervals of possible values.
result Clearly expresses epistemic uncertainty in predictions.
Improved confidence bounds for linear logistic model with applications to bandits.
problem Improving confidence bounds for linear logistic model.
method Self-concordant analysis of the logistic loss to avoid dependence on worst-case variance.
result Significant improvement in confidence bounds, avoiding dependence on 1/κ. PIANO speeds up multinomial logistic regression solving.
problem Handling large datasets and many classes in logistic regression.
method Parallel iterative algorithm based on Majorization Minimization.
result PIANO converges to a stationary point of Multinomial and Sparse Multinomial Logistic Regression.
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…
A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.
problem Efficiently solving logistic regression in one pass over a data stream.
method Data-oblivious sketching approach that reduces data size to poly(μdlog n) weighted points.
result Sketching reduces data size significantly and provides approximation guarantees.
New method calibrates logistic regression tuning parameters.
problem Difficulty in calibrating tuning parameters in logistic regression.
method Simple tests along the tuning parameter path.
result Optimal guarantees for feature selection.
A new algorithm approximates logistic regression probabilities efficiently.
problem Efficiently approximating probabilities in logistic regression for large datasets.
method Randomized sampling-based algorithm with leverage scores.
result Accurate approximations to estimated probabilities with smaller sample sizes.
Develops first optimal algorithm for logistic bandits.
problem Pure exploration in logistic bandits.
method Logistic track-and-stop (Log-TS) algorithm.
result Asymptotically matches lower bound for expected sample complexity.
Quantum model discovery uses DQCs to solve equations from data.
problem Discovering differential equations from data using quantum computing.
method Differentiable quantum circuits (DQCs) to solve parameterized equations, regression on data and equations.
result Successful parameter inference and equation discovery on various systems.
A general nonlinear logistic equation has been proposed to model long-time saturation in industrial growth. An integral solution of this equation has been derived for any arbitrary degree of nonlinearity. A time scale for the onset of nonlinear saturation in industrial growth can be estimated from an equipartition cond…
This paper analyzes stability of decision trees and logistic regression.
problem Stability of decision trees and logistic regression is analyzed to understand their performance and sensitivity.
method Two stability notions (hypothesis and pointwise hypothesis stability) are derived for decision trees and logistic regression. The stability of decision trees depends on the number of leaves, while for logistic regression, it depends on the smallest eigenvalue of the Hessian matrix. Upper bounds on generalization error are constructed.
result Logistic regression is not a stable learning algorithm.
Study fits BTC future returns from inverse options using logistic distribution.
problem Modeling future price distribution of Bitcoin.
method Fits empirical BTC future returns with logistic distribution using inverse options prices.
result BTC future returns can be described with a logistic distribution, but not stochastically.
Paper explores connections between loss functions and consistency in binary classification and regression.
problem Consistency in binary classification and regression applications.
method Characterization of conformable loss functions and derivation of a new Huber-type loss function.
result Margin-based loss functions are equivalent to loss functions of squared standardized logistic regression residuals.