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Found 9 papers, 4 papers with code
mlr3summary: Concise and interpretable summaries for machine learning models
This work introduces a novel R package for concise, informative summaries of machine learning models.
A Guide to Feature Importance Methods for Scientific Inference
Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide, due to their opaque internal mechanisms.
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks' parameter space.
Evaluating machine learning models in non-standard settings: An overview and new findings
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
A decision can be defined as fair if equal individuals are treated equally and unequals unequally.
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations.
Automated wildlife image classification: An active learning tool for ecological applications
(2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images.
What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds
A growing body of literature in fairness-aware ML (fairML) aspires to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM) by defining metrics that measure fairness of an ML model and by proposing methods that ensure that trained ML models achieve low values in those metrics.
Developing Open Source Educational Resources for Machine Learning and Data Science
It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS).
