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Interpretability

7,318 papers

Papers per year

Papers

Concept-based Explanations for Out-of-Distribution Detectors ICML 2023
A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations ICML 2023
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs ICML 2023
Meta-Learning the Inductive Bias of Simple Neural Circuits ICML 2023
Learning Perturbations to Explain Time Series Predictions ICML 2023
Parallel Neurosymbolic Integration with Concordia ICML 2023
Explainable Data-Driven Optimization: From Context to Decision and Back Again ICML 2023
Towards Reliable Neural Specifications ICML 2023
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat ICML 2023
Looped Transformers as Programmable Computers ICML 2023
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network ICML 2023
Conformal Prediction Sets for Graph Neural Networks ICML 2023
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees ICML 2023
On the Impact of Knowledge Distillation for Model Interpretability ICML 2023
Generalized Teacher Forcing for Learning Chaotic Dynamics ICML 2023
Decoding Layer Saliency in Language Transformers ICML 2023
Detecting Out-of-distribution Data through In-distribution Class Prior ICML 2023
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents ICML 2023
Automatically Auditing Large Language Models via Discrete Optimization ICML 2023
Identifying Interpretable Subspaces in Image Representations ICML 2023
On the Relationship Between Explanation and Prediction: A Causal View ICML 2023
PAC Prediction Sets for Large Language Models of Code ICML 2023
Trainability, Expressivity and Interpretability in Gated Neural ODEs ICML 2023
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten ICML 2023
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value ICML 2023