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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 537 papers

TitleStatusHype
GAMformer: In-Context Learning for Generalized Additive Models—0
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
A Semiparametric Approach to Interpretable Machine Learning—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
A Learning Theoretic Perspective on Local Explainability—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—0
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
Data-driven model reconstruction for nonlinear wave dynamics—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
Deducing neighborhoods of classes from a fitted model—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Comparing interpretability and explainability for feature selection—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Full interpretable machine learning in 2D with inline coordinates—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
A Novel Memetic Strategy for Optimized Learning of Classification Trees—0
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Additive Higher-Order Factorization Machines—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explanations for Automatic Speech Recognition—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Challenges in Variable Importance Ranking Under Correlation—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Causal rule ensemble approach for multi-arm data—0
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data—0
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Benchmark Results

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