conftrace_
← Core Methods
Machine Learning › Core Methods ›

Interpretability

499 papers

Papers per year

3
65
64
'15
'20
'25

Papers

Exploring the Latent Space of Autoencoders with Interventional Assays NIPS 2022
Learning Recourse on Instance Environment to Enhance Prediction Accuracy NIPS 2022
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network NIPS 2022
Explaining Preferences with Shapley Values NIPS 2022
Tsetlin Machine for Solving Contextual Bandit Problems NIPS 2022
First is Better Than Last for Language Data Influence NIPS 2022
VICE: Variational Interpretable Concept Embeddings NIPS 2022
WeightedSHAP: analyzing and improving Shapley based feature attributions NIPS 2022
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF NIPS 2022
Fuzzy Learning Machine NIPS 2022
Scalable Interpretability via Polynomials NIPS 2022
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers AAAI 2022
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks AAAI 2022
Interpretable Generative Adversarial Networks AAAI 2022
Optimizing Binary Decision Diagrams with MaxSAT for Classification AAAI 2022
How to Find a Good Explanation for Clustering? AAAI 2022
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations AAAI 2022
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles AAAI 2022
Trading Complexity for Sparsity in Random Forest Explanations AAAI 2022
Sufficient Reasons for Classifier Decisions in the Presence of Domain Constraints AAAI 2022
Entropy-Based Logic Explanations of Neural Networks AAAI 2022
Scaling Up Influence Functions AAAI 2022
Constraint-Driven Explanations for Black-Box ML Models AAAI 2022
Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models AAAI 2022
Rethinking Influence Functions of Neural Networks in the Over-Parameterized Regime AAAI 2022