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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–350 of 537 papers

TitleStatusHype
Linguistically inspired roadmap for building biologically reliable protein language models—0
Knowledge Discovery from Atomic Structures using Feature Importances—0
Knowledge Representation with Conceptual Spaces—0
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems—0
LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models—0
Learning Discrete Concepts in Latent Hierarchical Models—0
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models—0
Structural Node Embeddings with Homomorphism Counts—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Learning Kolmogorov Models for Binary Random Variables—0
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Learning Model Agnostic Explanations via Constraint Programming—0
Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator—0
A Survey of Malware Detection Using Deep Learning—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Subgroup Analysis via Model-based Rule Forest—0
A Semiparametric Approach to Interpretable Machine Learning—0
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning—0
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Using an interpretable Machine Learning approach to study the drivers of International Migration—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Techniques for Interpretable Machine Learning—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans—0
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
Additive Higher-Order Factorization Machines—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Tensor Polynomial Additive Model—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
MAntRA: A framework for model agnostic reliability analysis—0
The Doctor Just Won't Accept That!—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry—0
MCCE: Missingness-aware Causal Concept Explainer—0
Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability—0
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments—0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP—0
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?—0
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Benchmark Results

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