:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Fang, Haimo, Tan, Kevin, Pipping-Gamon, Jonathan, Hooker, Giles
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2601.18857
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Statistical Inference for Gradient Boosting Regression
by: Fang, Haimo, et al.
Published: (2025)

Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer
by: Pipping-Gamón, Jonathan, et al.
Published: (2025)

Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation
by: Tan, Kevin, et al.
Published: (2024)

Statistical Significance of Feature Importance Rankings
by: Goldwasser, Jeremy, et al.
Published: (2024)

Kicking for Goal or Touch? An Expected Points Framework for Penalty Decisions in Rugby Union
by: Watts, Kenny, et al.
Published: (2025)

Extreme-Path Benchmarks for Sequential Probability Forecasts
by: Pipping-Gamón, Jonathan, et al.
Published: (2026)

Estimating Implicit Regularization in Deep Learning
by: Rudoler, Joseph H., et al.
Published: (2026)

Stabilizing Estimates of Shapley Values with Control Variates
by: Goldwasser, Jeremy, et al.
Published: (2023)

Targeted Learning for Data Fairness
by: Asemota, Alexander, et al.
Published: (2025)

Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
by: Xin, Xi, et al.
Published: (2024)

Longitudinal Counterfactuals: Constraints and Opportunities
by: Asemota, Alexander, et al.
Published: (2024)

Targeted Maximum Likelihood Estimation for Integral Projection Models in Population Ecology
by: Zhou, Yunzhe, et al.
Published: (2024)

Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks
by: Goldwasser, Jeremy, et al.
Published: (2025)

Targeted Learning for Variable Importance
by: Wang, Xiaohan, et al.
Published: (2024)

A Unified Server Quality Metric for Tennis
by: Li, Aiwen, et al.
Published: (2026)

Statistical Inference under Adaptive Sampling with LinUCB
by: Fan, Wei, et al.
Published: (2025)

Rethinking Explainable Machine Learning as Applied Statistics
by: Bordt, Sebastian, et al.
Published: (2024)

Pushing the Boundaries of Interpretability: Incremental Enhancements to the Explainable Boosting Machine
by: Liyanage, Isara, et al.
Published: (2025)

Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance
by: Krùpovà, Markéta, et al.
Published: (2025)

Opponent-Adjusted Evaluation of NFL Pass Blocking and Pass Rushing Performance
by: Pipping-Gamón, Jonathan, et al.
Published: (2026)

Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
by: Yousif, Yasin, et al.
Published: (2024)

Statistical Inference for Clustering-based Anomaly Detection
by: Phu, Nguyen Thi Minh, et al.
Published: (2025)

Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine
by: Fazel, Reza E., et al.
Published: (2026)

xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
by: Burk, Lukas, et al.
Published: (2026)

Statistical Inference for Sequential Feature Selection after Domain Adaptation
by: Loc, Duong Tan, et al.
Published: (2025)

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
by: Dalmasso, Niccolò, et al.
Published: (2021)

Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction
by: Zhang, Xu, et al.
Published: (2026)

DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
by: Salmanian, Parisa, et al.
Published: (2024)

Valid Inference for Machine Learning Model Parameters
by: Dey, Neil, et al.
Published: (2023)

Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation
by: Loi, Nguyen Thang, et al.
Published: (2024)

EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning
by: Cho, Ha Na, et al.
Published: (2026)

Knowledge-Guided Machine Learning: Illustrating the use of Explainable Boosting Machines to Identify Overshooting Tops in Satellite Imagery
by: Mitchell, Nathan, et al.
Published: (2025)

Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials
by: Yu, Han, et al.
Published: (2024)

On the Limitations of Compute Thresholds as a Governance Strategy
by: Hooker, Sara
Published: (2024)

Active Statistical Inference
by: Zrnic, Tijana, et al.
Published: (2024)

Statistical Inference for Responsiveness Verification
by: Cheon, Seung Hyun, et al.
Published: (2025)

Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning
by: Dindorf, Carlo, et al.
Published: (2025)

Statistical Inference for Fuzzy Clustering
by: Wu, Qiuyi, et al.
Published: (2026)

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
by: Imai, Kosuke, et al.
Published: (2022)

Learning U-Statistics with Active Inference
by: Wang, Xiaoning, et al.
Published: (2026)