Probabilistic Linear Explanations

arXiv:2609.19077 · cs.LG, cs.AI · Submitted 2026-09-16 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Under Review

Code: https://github.com/FredericKoriche/ProbabilisticLinearExplanations

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 80× in this paper · explained in 2 episodes
• Sparsity — 49× in this paper · explained in 4 episodes
• Benchmark — 32× in this paper · explained in 2 episodes
• Explainability — 19× in this paper · explained in 3 episodes
• Sample Complexity — 13× in this paper · explained in 15 episodes
• Iterative Hard Thresholding — 12× in this paper
• Interpretability — 11× in this paper · explained in 15 episodes
• Generalization — 9× in this paper · explained in 13 episodes
• Model-Agnostic — 9× in this paper · explained in 2 episodes
• Unified Framework — 8× in this paper · explained in 5 episodes
• Admissibility — 7× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Mixed Integer Programming — 6× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• Loss Function — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Hamming Distance — 4× in this paper · explained in 2 episodes
• Conference on Uncertainty in Artificial Intelligence — 3× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• Convergence Guarantees — 3× in this paper · explained in 3 episodes
• Restricted Strong Convexity — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• SPARSE L INEAR E XPLANATION — 2× in this paper
• Shapley values — 2× in this paper · explained in 2 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Debt-to-Income Ratio — 1× in this paper
• Greedy Selector and Hyperplane Projector — 1× in this paper
• International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• International Joint Conference on Artificial Intelligence — 1× in this paper
• Restricted Isometry Property — 1× in this paper
• Restricted Strong Smoothness — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Inapproximability of sufficient reasons for decision trees
• Lower bounds over Boolean inputs for deep neural networks with ReLU gates
• How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation

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