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.
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.
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.
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.
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.
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 1/κ 1/ κ . 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.
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 regret bounds for logistic bandits via novel confidence set construction.
problem Dependencies in parameter space for logistic bandits, especially when S ≥ d S \geq d S ≥ d . method Regret-to-confidence-set conversion (R2CS) to construct convex confidence sets.
result Strict improvement in regret bound w.r.t. S S S in logistic bandits. 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.
Prevalidated ridge regression simplifies logistic regression for high-dimensional data.
problem Efficient probabilistic classification in high-dimensional data with logistic regression.
method Developed a prevalidated ridge regression model that matches logistic regression's performance but is more computationally efficient.
result Prevalidated ridge regression achieves similar classification error and log-loss to logistic regression for high-dimensional data.
Study shows sample complexity for logistic regression with normal covariates.
problem Estimating parameters of logistic regression with normal design.
method Analyzes sample complexity in terms of dimension and inverse temperature.
result Shows two change-points in sample complexity curve based on inverse temperature.
An ensemble approach learns vector-weighted formulae for RLR.
problem Learning Relational Logistic Regression (RLR) with vector-weighted features.
method Functional-gradient boosting methods for probabilistic logic models.
result Our approach outperforms other methods for learning RLR.
Combines response categories in multinomial logistic regression models.
problem Handling multiple response categories in logistic regression models.
method Penalized likelihood method with alternating direction method of multipliers.
result Encourages response category combination in the model.
Estimates future ICU demand using age-structured data and logistic modeling.
problem Projecting future ICU demand from low-quality data.
method Age-structured sampling, logistic fit, subnotification factor correction.
result Corrected logistic curve projects future ICU demand.
Proposes a model selection procedure for high-dimensional binary classification using sparse logistic regression.
problem High-dimensional binary classification with sparse logistic regression.
method Penalized maximum likelihood with complexity penalty on model size, Slope estimator for logistic regression.
result Proposed complexity penalty is rate-optimal in the minimax sense.
Improved classifier for PU data using logistic regression.
problem Analysis of Positive Unlabeled data under SCAR assumption.
method Fitting misspecified logistic regression model to PU data.
result The classifier performs on par or better than competitors on real data sets.
Study uses logistic regression and association rules to identify early symptoms of malignant mesothelioma.
problem Difficult diagnosis of malignant mesothelioma leading to late-stage detection and poor patient survival.
method Implemented logistic regression and developed association rules to identify early symptoms.
result Categorical logistic regression improved training accuracy from 72.30% to 81.40%.
Proposes a logistic regression method for manufacturing processes.
problem Logistic regression for multiple-instance data.
method Expectation-maximization algorithm with LASSO penalty.
result Identifies important covariates in manufacturing processes.
Logistic LDA applies LDA to non-categorical data.
problem Applying LDA to non-categorical data is challenging.
method Logistic LDA, a discriminative variant of LDA.
result Logistic LDA can learn from unlabeled data.
Improves logistic regression performance with nonconvex programming.
problem Stochastic generalized linear regression with chance constraints.
method Nonconvex programming techniques, clustering, quantile estimation.
result Over 1 to 2 percent improvement in model performance.
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 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.
Proposes a new method for logistic PCA to avoid overfitting.
problem Overfitting in logistic PCA for binary data.
method Non-convex singular value thresholding for logistic PCA.
result Proposed method outperforms models with convex penalties.
PbP strategy improves logistic model prediction with missing values.
problem Predicting with missing inputs in logistic models.
method Pattern-by-Pattern (PbP) strategy for logistic models with missing values.
result PbP accurately approximates Bayes probabilities under GPMM across various missing data scenarios.
The paper shows how variable discretization and cost-sensitive logistic regression improve credit scoring models on imbalanced data.
problem Bias in classification models on imbalanced datasets.
method Variable discretization and cost-sensitive logistic regression.
result Improves model performance on imbalanced credit scoring data and other domains.
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.
Efficient logistic regression for aggregated data reduces computation time.
problem Inference for logistic regression models is computationally expensive for large datasets.
method Adapted symbolic data analysis to summarise predictor variables into histograms and use composite likelihoods.
result The method achieves comparable classification rates to full data analysis but at lower computational cost.
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.
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.
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.
Paper compares SVM and Bayesian Logistic Regression for breast cancer diagnosis.
problem Improving breast cancer diagnosis accuracy using machine learning.
method Used SVM, Bayesian Logistic Regression, and K-Nearest-Neighbors for classification.
result SVM outperformed other classifiers, closely matched by Bayesian Logistic Regression.
The thesis examines stochastic calculus in option pricing with logistic models and numerical methods.
problem Exploring the application of stochastic calculus in option pricing.
method Monte-Carlo Simulation and machine learning algorithms.
result Insights from Peter Carr and Lorenzo Torricelli's convex duality in continuous models.
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 develops a learning algorithm for relational logistic regression.
problem Learning multi-relational data models.
method Structure learning with Schmidt and Murphy's hierarchical assumption and parameter learning using logistic regression.
result The learning algorithm improves RLR model performance compared to other methods.
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…
New algorithm reduces regret for logistic bandits without κ κ κ dependency.
problem Logistic bandits have poor frequentist regret guarantees due to large κ κ κ . method Optimistic algorithm based on self-normalized martingale tail-inequality.
result Achieves i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) regret with no κ κ κ dependency. A combined model integrates latent factor and logistic regression for citation network analysis.
problem Insufficient representation by either latent factor or logistic regression alone.
method Proposes a combined model integrating latent factor and logistic regression, with parameter estimation through joint-likelihood and penalty terms.
result The proposed method captures both main technological trends and ad-hoc dependencies in citation networks.
New framework optimizes classification trees with logistic loss and ℓ 1 \ell_1 ℓ 1 regularization.
problem Improving interpretability and generalization of classification trees.
method Developed a generalized framework for CTs, incorporating logistic loss and ℓ 1 \ell_1 ℓ 1 regularization. result Optimal Logistic Tree model outperforms state-of-the-art MIP-based approaches in terms of interpretability and generalization.
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 a semi-supervised learning method using exponential tilt mixture models.
problem Improves classification accuracy with labeled and unlabeled data.
method Extends logistic regression to exponential tilt modeling, derives maximum likelihood estimation, and proposes regularized estimation.
result Demonstrates improved prediction accuracy compared to existing methods.
A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logis…
Optimizes link prediction using matrix logistic regression.
problem Predicting links in networks with limited data.
method Formulated as matrix logistic regression, analyzed in high dimensions, combinatorial estimator with penalized maximum likelihood.
result Achieves minimax rate for Frobenius-norm risk, cannot be computed efficiently.
New method tackles parcel routing with AI.
problem Routing parcels efficiently through a network of hubs.
method Combines graph neural networks with model-free RL.
result Extracts small feature graphs from the environment state.
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.
We analyze the optimization landscape of α-loss in logistic models.
problem Optimization landscape of α-loss in logistic models.
method Tools from strictly-locally-quasi-convex functions and geometric techniques.
result Evolution of optimization landscape with respect to α.
Sparse multinomial logistic regression for multiclass classification with feature selection.
problem High-dimensional multiclass classification with a focus on sparse models.
method Penalized maximum likelihood with complexity penalty, feature selection using group Lasso and Slope classifiers.
result Achievement of minimax order in both small and large number of classes regimes.
Machine learning improves joint default assessment by capturing non-linear dependencies.
problem Capturing non-linear dependencies among covariates for accurate joint default assessment.
method Application of machine learning techniques to credit card dataset, comparing with logistic regression.
result Machine learning outperforms logistic regression in assessing portfolio riskiness.