Cover Image

Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Freiesleben, Timo, König, Gunnar, Molnar, Christoph, Tejero-Cantero, Alvaro
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Benchmarking Epistemology: Construct Validity for Evaluating Machine Learning Models
by: Freiesleben, Timo, et al.
Published: (2025)

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
by: Freiesleben, Timo, et al.
Published: (2026)

Improvement-Focused Causal Recourse (ICR)
by: König, Gunnar, et al.
Published: (2022)

Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks
by: Freiesleben, Timo
Published: (2026)

Artificial Neural Nets and the Representation of Human Concepts
by: Freiesleben, Timo
Published: (2023)

Performative Validity of Recourse Explanations
by: König, Gunnar, et al.
Published: (2025)

CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
by: Dandl, Susanne, et al.
Published: (2024)

Predictive, scalable and interpretable knowledge tracing on structured domains
by: Zhou, Hanqi, et al.
Published: (2024)

Analyzing Inference Privacy Risks Through Gradients in Machine Learning
by: Li, Zhuohang, et al.
Published: (2024)

A Guide to Feature Importance Methods for Scientific Inference
by: Ewald, Fiona Katharina, et al.
Published: (2024)

Benchmarking Scientific Machine Learning Models for Air Quality Data
by: Masud, Khawja Imran, et al.
Published: (2026)

Review of Interpretable Machine Learning Models for Disease Prognosis
by: Shen, Jinzhi, et al.
Published: (2024)

Enriching the Machine Learning Workloads in BigBench
by: Polag, Matthias, et al.
Published: (2024)

Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
by: Chang, Tyler, et al.
Published: (2024)

Scientific Machine Learning Seismology
by: Okazaki, Tomohisa
Published: (2024)

Analyzing Fairness of Classification Machine Learning Model with Structured Dataset
by: Rashed, Ahmed, et al.
Published: (2024)

Physics-Inspired Interpretability Of Machine Learning Models
by: Niroomand, Maximilian P, et al.
Published: (2023)

GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
by: Mueller, Andreas, et al.
Published: (2024)

Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
by: Smart, Andrew, et al.
Published: (2024)

Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation
by: Starostin, Vladimir, et al.
Published: (2024)

Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting
by: Jiang, Qian, et al.
Published: (2026)

Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
by: Aguilar-Bejarano, Eduardo, et al.
Published: (2025)

Best Practices for Machine Learning Experimentation in Scientific Applications
by: Michelucci, Umberto, et al.
Published: (2025)

Physically Interpretable World Models via Weakly Supervised Representation Learning
by: Mao, Zhenjiang, et al.
Published: (2024)

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

Machine Learning based Analysis for Radiomics Features Robustness in Real-World Deployment Scenarios
by: Khan, Sarmad Ahmad, et al.
Published: (2025)

Quantum Machine Learning: Performance and Security Implications in Real-World Applications
by: Luo, Zhengping Jay, et al.
Published: (2024)

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
by: Katta, Srikar, et al.
Published: (2023)

Dual Interpretation of Machine Learning Forecasts
by: Coulombe, Philippe Goulet, et al.
Published: (2024)

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

Interpreting Black-box Machine Learning Models for High Dimensional Datasets
by: Karim, Md. Rezaul, et al.
Published: (2022)

SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data
by: Zhang, Mingxing, et al.
Published: (2026)

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
by: Bertsimas, Dimitris, et al.
Published: (2024)

Probabilistic Scoring Lists for Interpretable Machine Learning
by: Hanselle, Jonas, et al.
Published: (2024)

Deep Learning on Real-World Graphs
by: Rossi, Emanuele
Published: (2025)

Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
by: Lu, Binghang, et al.
Published: (2026)

A Declarative Query Language for Scientific Machine Learning
by: Jamil, Hasan M
Published: (2024)

Modeling chaotic Lorenz ODE System using Scientific Machine Learning
by: Kashyap, Sameera S, et al.
Published: (2024)

Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
by: Rubel, Mominul, et al.
Published: (2025)

FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization
by: Feng, Shengyu, et al.
Published: (2025)