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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
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering—0
Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients—0
Shapley variable importance cloud for machine learning models—0
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity—0
Structural Node Embeddings with Homomorphism Counts—0
Subgroup Analysis via Model-based Rule Forest—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Techniques for Interpretable Machine Learning—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Tensor Polynomial Additive Model—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—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
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
The Partial Response Network: a neural network nomogram—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods In Psychiatry Detection Applications, Specifically Depression Disorder: A Brief Review.—0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review—0
Topological data analysis of zebrafish patterns—0
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications—0
Toward More Generalized Malicious URL Detection Models—0
Towards Analogy-Based Explanations in Machine Learning—0
Towards A Rigorous Science of Interpretable Machine Learning—0
Interpretable Machine Learning: Moving From Mythos to Diagnostics—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Towards making NLG a voice for interpretable Machine Learning—0
Towards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems—0
Towards Simple Machine Learning Baselines for GNSS RFI Detection—0
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning—0
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Using an interpretable Machine Learning approach to study the drivers of International Migration—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—0
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models—0
Variable Selection via Thompson Sampling—0
Leveraging advances in machine learning for the robust classification and interpretation of networks—0
Who will dropout from university? Academic risk prediction based on interpretable machine learning—0
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning—0
Hidden Citations Obscure True Impact in Science—0
High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model—0
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Benchmark Results

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