:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Naser, MZ
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Methodology
Online Access:	https://arxiv.org/abs/2501.04894
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
by: Naser, Ahmed Z, et al.
Published: (2024)

Philosophy-informed Machine Learning
by: Naser, MZ
Published: (2025)

Robustly estimating heterogeneity in factorial data using Rashomon Partitions
by: Venkateswaran, Aparajithan, et al.
Published: (2024)

Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding
by: Peña, Jose M.
Published: (2024)

SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems
by: Naser, MZ, et al.
Published: (2024)

Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning
by: Balzer, Laura B., et al.
Published: (2025)

SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection
by: Naser, MZ, et al.
Published: (2024)

Why are there many equally good models? An Anatomy of the Rashomon Effect
by: Parikh, Harsh
Published: (2026)

Interpretable Machine Learning for Survival Analysis
by: Langbein, Sophie Hanna, et al.
Published: (2024)

SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
by: Naser, MZ, et al.
Published: (2024)

Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
by: Fuhr, Jonathan, et al.
Published: (2024)

Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach
by: Mirthipati, Tejas
Published: (2024)

Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)

A Closer Look at AUROC and AUPRC under Class Imbalance
by: McDermott, Matthew B. A., et al.
Published: (2024)

Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
by: Blocher, Hannah, et al.
Published: (2023)

Principal Component Analysis When n < p: Challenges and Solutions
by: Weeraratne, Nuwan, et al.
Published: (2025)

Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
by: Gao, Zhaoxing, et al.
Published: (2024)

A Primer on the Signature Method in Machine Learning
by: Chevyrev, Ilya, et al.
Published: (2016)

Knowledge Distillation Decision Tree for Unravelling Black-box Machine Learning Models
by: Lu, Xuetao, et al.
Published: (2022)

Causal Machine Learning: A Survey and Open Problems
by: Kaddour, Jean, et al.
Published: (2022)

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
by: Park, Soojin, et al.
Published: (2025)

Optimal Discriminant Analysis in High-Dimensional Latent Factor Models
by: Bing, Xin, et al.
Published: (2022)

Optimal Model Selection for Conformalized Robust Optimization
by: Bao, Yajie, et al.
Published: (2025)

A Systematic Bias of Machine Learning Regression Models and Its Correction: an Application to Imaging-based Brain Age Prediction
by: Lee, Hwiyoung, et al.
Published: (2024)

Imputation Uncertainty in Interpretable Machine Learning Methods
by: Golchian, Pegah, et al.
Published: (2025)

Task-Agnostic Machine-Learning-Assisted Inference
by: Miao, Jiacheng, et al.
Published: (2024)

Causal Representation Learning with Optimal Compression under Complex Treatments
by: Liang, Wanting, et al.
Published: (2026)

Aggregation Models with Optimal Weights for Distributed Gaussian Processes
by: Chen, Haoyuan, et al.
Published: (2024)

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
by: Chen, Xingran, et al.
Published: (2025)

Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
by: Park, Soojin, et al.
Published: (2025)

Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
by: Park, Sihyung, et al.
Published: (2025)

A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Machine Learning and Statistical Modeling
by: Siddiqui, Md. Saiful Bari, et al.
Published: (2025)

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
by: Trivedi, Shubhendu, et al.
Published: (2025)

What if? Causal Machine Learning in Supply Chain Risk Management
by: Wyrembek, Mateusz, et al.
Published: (2024)

M-SGWR: Multiscale Similarity and Geographically Weighted Regression
by: Lessani, M. Naser, et al.
Published: (2026)

Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
by: Kang, Lulu, et al.
Published: (2025)

A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes
by: Salmaso, Filippo, et al.
Published: (2026)

An Introduction to Double/Debiased Machine Learning
by: Ahrens, Achim, et al.
Published: (2025)

Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection
by: Kar, Ankan, et al.
Published: (2024)

Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
by: Shao, Daqian, et al.
Published: (2025)