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 26–50 of 537 papers

TitleStatusHype
Causal rule ensemble approach for multi-arm data—0
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution AnalysisCode0
Towards Simple Machine Learning Baselines for GNSS RFI Detection—0
Interpretable Machine Learning in Physics: A Review—0
Kernel Learning Assisted Synthesis Condition Exploration for Ternary SpinelCode0
Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning—0
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change—0
Predicting and Understanding College Student Mental Health with Interpretable Machine LearningCode0
Diagnostic-free onboard battery health assessment—0
A Frank System for Co-Evolutionary Hybrid Decision-Making—0
Near Optimal Decision Trees in a SPLIT Second—0
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions—0
Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning—0
Interpretable Machine Learning for Kronecker Coefficients—0
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine LearningCode0
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic DataCode0
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island CoevolutionCode0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
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Benchmark Results

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