Cover Image

Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Troisemaine, Colin, Lemaire, Vincent
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Practical Approach to Novel Class Discovery in Tabular Data
by: Troisemaine, Colin, et al.
Published: (2023)

Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series
by: Renault, Aurélien, et al.
Published: (2025)

DEMAU: Decompose, Explore, Model and Analyse Uncertainties
by: Hoarau, Arthur, et al.
Published: (2024)

Interpretable Quantile Regression by Optimal Decision Trees
by: Lemaire, Valentin, et al.
Published: (2026)

A new Input Convex Neural Network with application to options pricing
by: Lemaire, Vincent, et al.
Published: (2024)

Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation
by: Hamon, Marine, et al.
Published: (2025)

Temporal Sepsis Modeling: a Relational and Explainable-by-Design Framework
by: Lemaire, Vincent, et al.
Published: (2026)

Early Classification of Time Series: A Survey and Benchmark
by: Renault, Aurélien, et al.
Published: (2024)

Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
by: George, Thomas, et al.
Published: (2024)

Calibration improves detection of mislabeled examples
by: Chibane, Ilies, et al.
Published: (2025)

Early Classification of Time Series in Non-Stationary Cost Regimes
by: Renault, Aurélien, et al.
Published: (2026)

Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers
by: Lemaire, Vincent, et al.
Published: (2023)

Deep Copula Classifier: Theory, Consistency, and Empirical Evaluation
by: Aich, Agnideep, et al.
Published: (2025)

On Forgetting and Stability of Score-based Generative models
by: Strasman, Stanislas, et al.
Published: (2026)

Geometry-Aware Instrumental Variable Regression
by: Kremer, Heiner, et al.
Published: (2024)

Naive Bayes Classifiers and One-hot Encoding of Categorical Variables
by: Williams, Christopher K. I.
Published: (2024)

Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers
by: C, Ramya, et al.
Published: (2021)

Evidential uncertainty sampling for active learning
by: Hoarau, Arthur, et al.
Published: (2023)

An analysis of the noise schedule for score-based generative models
by: Strasman, Stanislas, et al.
Published: (2024)

Accurate Coresets for Latent Variable Models and Regularized Regression
by: Ranjan, Sanskar, et al.
Published: (2024)

Scalable Neural Symbolic Regression using Control Variables
by: Chu, Xieting, et al.
Published: (2023)

Shrinkage-Based Regressions with Many Related Treatments
by: Dilber, Enes, et al.
Published: (2025)

ml_edm package: a Python toolkit for Machine Learning based Early Decision Making
by: Renault, Aurélien, et al.
Published: (2024)

Probabilistic Traffic Forecasting with Dynamic Regression
by: Zheng, Vincent Zhihao, et al.
Published: (2023)

Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
by: Fonseca, Yuri, et al.
Published: (2024)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
by: Kim, Juno, et al.
Published: (2025)

Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
by: Meunier, Dimitri, et al.
Published: (2025)

Inference for Regression with Variables Generated by AI or Machine Learning
by: Battaglia, Laura, et al.
Published: (2024)

Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression
by: Wu, Anpeng, et al.
Published: (2024)

Demystifying Spectral Feature Learning for Instrumental Variable Regression
by: Meunier, Dimitri, et al.
Published: (2025)

Learning Treatment Representations for Downstream Instrumental Variable Regression
by: Lin, Shiangyi, et al.
Published: (2025)

Nested Nonparametric Instrumental Variable Regression
by: Meza, Isaac, et al.
Published: (2021)

Nonparametric Instrumental Variable Regression with Observed Covariates
by: Shen, Zikai, et al.
Published: (2025)

Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers
by: Rajhans, Mona, et al.
Published: (2026)

Empirical Evidence for Simply Connected Decision Regions in Image Classifiers
by: Swaminathan, Arjhun, et al.
Published: (2026)

Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable
by: Wang, Zhanfeng, et al.
Published: (2024)

Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks
by: Zhu, Wenbin, et al.
Published: (2024)

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback
by: Della Vecchia, Riccardo, et al.
Published: (2023)

Understanding Pathologies of Deep Heteroskedastic Regression
by: Wong-Toi, Eliot, et al.
Published: (2023)

Wasserstein Convergence of Critically Damped Langevin Diffusions
by: Strasman, Stanislas, et al.
Published: (2025)