The Signed Geometry of One-Shot Recourse: On-Path Validity and the Signed-Curvature Criterion

arXiv:2609.36252 · cs.LG, stat.ML · Submitted 2026-09-28 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-28

Updated: 2026-10-06

Terminology

• alpha — 75× in this paper · explained in 1 episode
• Calibration — 44× in this paper · explained in 7 episodes
• Accuracy — 41× in this paper · explained in 1 episode
• Robustness — 22× in this paper · explained in 5 episodes
• Abstention — 13× in this paper · explained in 1 episode
• Adam — 10× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Conformal Prediction — 5× in this paper · explained in 5 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Adversarial Robustness — 4× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Exchangeability — 4× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 4× in this paper
• Provenance — 4× in this paper · explained in 4 episodes
• Accountability — 3× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Actionability — 2× in this paper · explained in 3 episodes
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Covariate Shift — 2× in this paper · explained in 2 episodes
• Gradient Flow — 2× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 2× in this paper
• Trace — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adaptivity — 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 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes

Sources

• Equalizing Recourse across Groups
• Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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