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 226–250 of 537 papers

TitleStatusHype
Efficient and quantum-adaptive machine learning with fermion neural networksCode0
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations—0
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine LearningCode0
Motif-guided Time Series Counterfactual Explanations—0
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
Margin Optimal Classification TreesCode0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Conditional Feature Importance for Mixed DataCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
Understanding Interventional TreeSHAP : How and Why it WorksCode0
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsCode0
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devicesCode0
TSInterpret: A unified framework for time series interpretabilityCode1
Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models—0
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—0
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process—0
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Benchmark Results

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