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interpretable machine learning

139 papers

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Also known as

XAI IML

Co-occurring keywords

interpretable model (158) decision tree (322) concept bottleneck model (41) neural network (6616) explainable ai (478) model interpretability (510) image classification (1943) concept bottleneck (29) text classification (6776) model explanation (154)

Papers

Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables NIPS 2024
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction NAACL 2024
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance NIPS 2024
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal ACL 2024
Human-Robot Alignment through Interactivity and Interpretability: Don't Assume a ``Spherical Human'' IJCAI 2024
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction AAAI 2024
Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis COLING 2024
ViTree: Single-Path Neural Tree for Step-Wise Interpretable Fine-Grained Visual Categorization AAAI 2024
On the Concept Trustworthiness in Concept Bottleneck Models AAAI 2024
Toward More Generalized Malicious URL Detection Models AAAI 2024
Atomic Inference for NLI with Generated Facts as Atoms EMNLP 2024
iTrendRNN: An Interpretable Trend-Aware RNN for Meteorological Spatiotemporal Prediction AAAI 2024
Incremental Residual Concept Bottleneck Models CVPR 2024
Online Learning of Decision Trees with Thompson Sampling AISTATS 2024
Pixel-Grounded Prototypical Part Networks WACV 2024
Interactive Mars Image Content-Based Search with Interpretable Machine Learning AAAI 2024
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models ACL 2024
Self-Interpretable Graph Learning with Sufficient and Necessary Explanations AAAI 2024
Stochastic Concept Bottleneck Models NIPS 2024
Finding Interpretable Class-Specific Patterns through Efficient Neural Search AAAI 2024
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents NIPS 2024
Driving Through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving WACV 2024
Learning Performance Maximizing Ensembles with Explainability Guarantees AAAI 2024
Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements ACL 2024
Thesis Summary: Operationalizing User-Inclusive Transparency in Artificial Intelligence Systems AAAI 2024