We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| sigmoid | Ran | this paper's copy was not recorded; identical code first harvested from camp-explain-ai/style-chexplain pointer only · get_code("c27e5c071e798245") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Classifier evasion consists in finding for a given instance $x$ the nearest instance $x'$ such that the classifier predictions of $x$ and $x'$ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixed Integer Linear Program solver and finds the optimal evading instance under an expressive set of constraints. Our second algorithm trades off optimality for speed by using symbolic prediction, a novel algorithm for fast finite differences on tree ensembles. On a digit recognition task, we demonstrate that both gradient boosted trees and random forests are extremely susceptible to evasions. Finally, we harden a boosted tree model without loss of predictive accuracy by augmenting the training set of each boosting round with evading instances, a technique we call adversarial boosting.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1509.07892")
get_code_for_paper("1509.07892")
have("1509.07892")
Connect an agent — have() is free.