SOTAVerified

Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 261–270 of 537 papers

TitleStatusHype
Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default—0
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach—0
On the Shape of Brainscores for Large Language Models (LLMs)—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Open Issues in Combating Fake News: Interpretability as an Opportunity—0
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic Attractors—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
Overcoming Catastrophic Forgetting by XAI—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified