Image de couverture de livre

Mixed-feature Logistic Regression Robust to Distribution Shifts

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Sun, Qingshi, Justin, Nathan, Gomez, Andres, Vayanos, Phebe
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Responsible Machine Learning via Mixed-Integer Optimization
par: Justin, Nathan, et autres
Publié: (2025)

Learning Optimal Classification Trees Robust to Distribution Shifts
par: Justin, Nathan, et autres
Publié: (2023)

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
par: Vossler, Patrick, et autres
Publié: (2023)

Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment
par: Tang, Bill, et autres
Publié: (2023)

Distributionally Robust Optimization with Decision-Dependent Information Discovery
par: Jin, Qing, et autres
Publié: (2024)

Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
par: Selvi, Aras, et autres
Publié: (2024)

Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression
par: Kale, Sacchit, et autres
Publié: (2026)

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
par: Meng, Si Yi, et autres
Publié: (2024)

Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere?
par: Meng, Si Yi, et autres
Publié: (2025)

It's All in the Mix: Wasserstein Classification and Regression with Mixed Features
par: Belbasi, Reza, et autres
Publié: (2023)

Binary Kernel Logistic Regression: a sparsity-inducing formulation and a convergent decomposition training algorithm
par: Consolo, Antonio, et autres
Publié: (2025)

Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning
par: Amortila, Philip, et autres
Publié: (2024)

Optimal Cross-Validation for Sparse Linear Regression
par: Cory-Wright, Ryan, et autres
Publié: (2023)

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
par: Chakrabarti, Kushal, et autres
Publié: (2021)

Robust Regression over Averaged Uncertainty
par: Bertsimas, Dimitris, et autres
Publié: (2023)

Learning Rate Schedules in the Presence of Distribution Shift
par: Fahrbach, Matthew, et autres
Publié: (2023)

Fair and Accurate Regression: Strong Formulations and Algorithms
par: Deza, Anna, et autres
Publié: (2024)

Distributionally Robust Optimization
par: Kuhn, Daniel, et autres
Publié: (2024)

Nonlinear Distributionally Robust Optimization
par: Sheriff, Mohammed Rayyan, et autres
Publié: (2023)

Distributionally-Robust Learning to Optimize
par: Ranjan, Vinit, et autres
Publié: (2026)

Sinkhorn Distributionally Robust Optimization
par: Wang, Jie, et autres
Publié: (2021)

Robust support vector machines via conic optimization
par: Cepeda, Valentina, et autres
Publié: (2024)

Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls
par: Wang, Tianyu, et autres
Publié: (2024)

Inverse Reinforcement Learning with Just Classification and a Few Regressions
par: van der Laan, Lars, et autres
Publié: (2025)

Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
par: Park, Jaehyun, et autres
Publié: (2024)

Wasserstein Distributionally Robust Regret Optimization
par: Fiechtner, Lukas-Benedikt, et autres
Publié: (2025)

Distributionally Robust Learning in Survival Analysis
par: Jin, Yeping, et autres
Publié: (2025)

Distributionally Robust Multi-Objective Optimization
par: Yang, Yufeng, et autres
Publié: (2026)

Wasserstein Distributionally Robust Online Learning
par: Chen, Guixian, et autres
Publié: (2026)

Robust Out-of-Distribution Stochastic Optimization
par: Li, Xianyu, et autres
Publié: (2026)

Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contamination
par: You, Yulei, et autres
Publié: (2026)

From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
par: Chan, Gabriel, et autres
Publié: (2024)

On the Foundation of Distributionally Robust Reinforcement Learning
par: Wang, Shengbo, et autres
Publié: (2023)

Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
par: Gopalani, Pulkit, et autres
Publié: (2023)

Distributionally Robust Federated Learning with Outlier Resilience
par: Wang, Zifan, et autres
Publié: (2025)

Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
par: Li, Shuyao, et autres
Publié: (2024)

Group Distributionally Robust Optimization with Flexible Sample Queries
par: Bai, Haomin, et autres
Publié: (2025)

Wasserstein Distributionally Robust Shallow Convex Neural Networks
par: Pallage, Julien, et autres
Publié: (2024)

On Generalization and Regularization via Wasserstein Distributionally Robust Optimization
par: Wu, Qinyu, et autres
Publié: (2022)

Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
par: Wang, Lei, et autres
Publié: (2025)