What is recorded here?
Adapted influence functions to estimate how individual training points affect learned parameters and predictions using gradients and Hessian-vector products, and evaluated diagnostic and attack uses.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact two-author ICML paper. Its classical assumptions can fail for non-convex or non-differentiable models, where the paper reports approximations rather than universal explanation guarantees.
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People and systems
Pang Wei Koh; Percy Liang
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Koh and Liang: understanding black-box predictions via influence functions
Adapted influence functions to estimate how individual training points affect learned parameters and predictions using gradients and Hessian-vector products, and evaluated diagnostic and attack uses.
Scientific scope
The record is the exact two-author ICML paper. Its classical assumptions can fail for non-convex or non-differentiable models, where the paper reports approximations rather than universal explanation guarantees.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Pang Wei Koh; Percy Liang
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.