Towards the Unification and Robustness of Perturbation and Gradient\n Based Explanations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As machine learning black boxes are increasingly being deployed in critical\ndomains such as healthcare and criminal justice, there has been a growing\nemphasis on developing techniques for explaining these black boxes in a post\nhoc manner. In this work, we analyze two popular post hoc interpretation\ntechniques: SmoothGrad which is a gradient based method, and a variant of LIME\nwhich is a perturbation based method. More specifically, we derive explicit\nclosed form expressions for the explanations output by these two methods and\nshow that they both converge to the same explanation in expectation, i.e., when\nthe number of perturbed samples used by these methods is large. We then\nleverage this connection to establish other desirable properties, such as\nrobustness, for these techniques. We also derive finite sample complexity\nbounds for the number of perturbations required for these methods to converge\nto their expected explanation. Finally, we empirically validate our theory\nusing extensive experimentation on both synthetic and real world datasets.\n
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it