Saved in:
| Main Authors: | Suttaket, Thiti, Vardhan, L Vivek Harsha, Kok, Stanley |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.03224 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
In-Training Multicalibrated Survival Analysis for Healthcare via Constrained Optimization
by: Suttaket, Thiti, et al.
Published: (2025)
by: Suttaket, Thiti, et al.
Published: (2025)
Practical Global and Local Bounds in Gaussian Process Regression via Chaining
by: Liu, Junyi, et al.
Published: (2025)
by: Liu, Junyi, et al.
Published: (2025)
Prediction of Bank Credit Ratings using Heterogeneous Topological Graph Neural Networks
by: Liu, Junyi, et al.
Published: (2025)
by: Liu, Junyi, et al.
Published: (2025)
Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay
by: Mandalika, Sriram, et al.
Published: (2025)
by: Mandalika, Sriram, et al.
Published: (2025)
Are Logistic Models Really Interpretable?
by: Dervovic, Danial, et al.
Published: (2024)
by: Dervovic, Danial, et al.
Published: (2024)
Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process
by: Williams, Bernardo, et al.
Published: (2026)
by: Williams, Bernardo, et al.
Published: (2026)
Wave Physics-informed Matrix Factorizations
by: Tetali, Harsha Vardhan, et al.
Published: (2023)
by: Tetali, Harsha Vardhan, et al.
Published: (2023)
A Logistic Regression Model to Predict Malaria Severity in Children
by: Ansong, Mary Opokua, et al.
Published: (2026)
by: Ansong, Mary Opokua, et al.
Published: (2026)
Grokking in Linear Models for Logistic Regression
by: Das, Nataraj, et al.
Published: (2026)
by: Das, Nataraj, et al.
Published: (2026)
Feature-Wise Mixing for Mitigating Contextual Bias in Predictive Supervised Learning
by: Tomar, Yash Vardhan
Published: (2025)
by: Tomar, Yash Vardhan
Published: (2025)
Explainable Risk Classification in Financial Reports
by: Tan, Xue Wen, et al.
Published: (2024)
by: Tan, Xue Wen, et al.
Published: (2024)
Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchical Transformer Network Approach
by: Tan, Xue Wen, et al.
Published: (2025)
by: Tan, Xue Wen, et al.
Published: (2025)
Compact Memory for Continual Logistic Regression
by: Jung, Yohan, et al.
Published: (2025)
by: Jung, Yohan, et al.
Published: (2025)
Efficient Logistic Regression with Mixture of Sigmoids
by: Di Gennaro, Federico, et al.
Published: (2026)
by: Di Gennaro, Federico, et al.
Published: (2026)
Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
by: Zhang, Ruiqi, et al.
Published: (2025)
by: Zhang, Ruiqi, et al.
Published: (2025)
F-measure Maximizing Logistic Regression
by: Okabe, Masaaki, et al.
Published: (2019)
by: Okabe, Masaaki, et al.
Published: (2019)
Semi-parametric Functional Classification via Path Signatures Logistic Regression
by: Zeng, Pengcheng, et al.
Published: (2025)
by: Zeng, Pengcheng, et al.
Published: (2025)
An Experiment on Feature Selection using Logistic Regression
by: Islam, Raisa, et al.
Published: (2024)
by: Islam, Raisa, et al.
Published: (2024)
Near-Polynomially Competitive Active Logistic Regression
by: Zhou, Yihan, et al.
Published: (2025)
by: Zhou, Yihan, et al.
Published: (2025)
Impact of Label Noise on Learning Complex Features
by: Vashisht, Rahul, et al.
Published: (2024)
by: Vashisht, Rahul, et al.
Published: (2024)
Data Science with LLMs and Interpretable Models
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
by: Zhang, Chenyang, et al.
Published: (2026)
by: Zhang, Chenyang, et al.
Published: (2026)
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
by: Selvi, Aras, et al.
Published: (2024)
by: Selvi, Aras, et al.
Published: (2024)
Riemannian Multinomial Logistics Regression for SPD Neural Networks
by: Chen, Ziheng, et al.
Published: (2023)
by: Chen, Ziheng, et al.
Published: (2023)
Meta-Continual Mobility Forecasting for Proactive Handover Prediction
by: Mandapati, Sasi Vardhan Reddy
Published: (2025)
by: Mandapati, Sasi Vardhan Reddy
Published: (2025)
LFFR: Logistic Function For (single-output) Regression
by: Chiang, John
Published: (2024)
by: Chiang, John
Published: (2024)
Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources
by: Ba, Amadou, et al.
Published: (2024)
by: Ba, Amadou, et al.
Published: (2024)
Data-Driven Logistic Regression Ensembles With Applications in Genomics
by: Christidis, Anthony-Alexander, et al.
Published: (2021)
by: Christidis, Anthony-Alexander, et al.
Published: (2021)
Interpretable Tabular Foundation Models via In-Context Kernel Regression
by: Miftachov, Ratmir, et al.
Published: (2026)
by: Miftachov, Ratmir, et al.
Published: (2026)
$L_1$-norm Regularized Indefinite Kernel Logistic Regression
by: Wang, Shaoxin, et al.
Published: (2025)
by: Wang, Shaoxin, et al.
Published: (2025)
Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)
by: Wu, Jingfeng, et al.
Published: (2025)
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)
by: Wu, Jingfeng, et al.
Published: (2025)
Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
by: Crawshaw, Michael, et al.
Published: (2025)
by: Crawshaw, Michael, et al.
Published: (2025)
Residue-based Label Protection Mechanisms in Vertical Logistic Regression
by: Tan, Juntao, et al.
Published: (2022)
by: Tan, Juntao, et al.
Published: (2022)
RMLR: Extending Multinomial Logistic Regression into General Geometries
by: Chen, Ziheng, et al.
Published: (2024)
by: Chen, Ziheng, et al.
Published: (2024)
Mixed-feature Logistic Regression Robust to Distribution Shifts
by: Sun, Qingshi, et al.
Published: (2025)
by: Sun, Qingshi, et al.
Published: (2025)
Scalable Kernel Logistic Regression with Nyström Approximation: Theoretical Analysis and Application to Discrete Choice Modelling
by: Martín-Baos, José Ángel, et al.
Published: (2024)
by: Martín-Baos, José Ángel, et al.
Published: (2024)
Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators
by: Deng, Ou, et al.
Published: (2025)
by: Deng, Ou, et al.
Published: (2025)
Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression
by: Kale, Sacchit, et al.
Published: (2026)
by: Kale, Sacchit, et al.
Published: (2026)
Are Linear Regression Models White Box and Interpretable?
by: Salih, Ahmed M, et al.
Published: (2024)
by: Salih, Ahmed M, et al.
Published: (2024)
Similar Items
-
In-Training Multicalibrated Survival Analysis for Healthcare via Constrained Optimization
by: Suttaket, Thiti, et al.
Published: (2025) -
Practical Global and Local Bounds in Gaussian Process Regression via Chaining
by: Liu, Junyi, et al.
Published: (2025) -
Prediction of Bank Credit Ratings using Heterogeneous Topological Graph Neural Networks
by: Liu, Junyi, et al.
Published: (2025) -
Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay
by: Mandalika, Sriram, et al.
Published: (2025) -
Are Logistic Models Really Interpretable?
by: Dervovic, Danial, et al.
Published: (2024)