feature attribution

Boundary-Aware Uncertainty for Feature Attribution Explainers AISTATS 2024 Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models NAACL 2024 CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems AAAI 2024 Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables NeurIPS 2024 Delivering Inflated Explanations AAAI 2024 Evaluating Saliency Explanations in NLP by Crowdsourcing COLING 2024 Explanations that reveal all through the definition of encoding NeurIPS 2024 Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation ICML 2024 FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors NeurIPS 2024 GiLOT: Interpreting Generative Language Models via Optimal Transport ICML 2024 GOAt: Explaining Graph Neural Networks via Graph Output Attribution ICLR 2024 Improving Decision Sparsity NeurIPS 2024 Latent Concept-based Explanation of NLP Models EMNLP 2024 LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations NAACL 2024 LLM Explainability via Attributive Masking Learning EMNLP 2024 LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models ACL 2024 Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution ICML 2024 Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics NeurIPS 2024 One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently NeurIPS 2024 Path Choice Matters for Clear Attributions in Path Methods ICLR 2024 Pathwise Explanation of ReLU Neural Networks AISTATS 2024 Prospector Heads: Generalized Feature Attribution for Large Models & Data ICML 2024 Provably Better Explanations with Optimized Aggregation of Feature Attributions ICML 2024 Rethinking Robustness of Model Attributions AAAI 2024 Selective Explanations NeurIPS 2024