Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Proving Data-Poisoning Robustness in Decision Trees   Machine learning models are brittle, and small changes in the training data
can result in different predictions. We study the problem of proving that a
prediction is robust to data poisoning, where an attacker can inject a number
of malicious elements into the training set to influence the learned model. We
target decision-tree models, a popular and simple class of machine learning
models that underlies many complex learning techniques. We present a sound
verification technique based on abstract interpretation and implement it in a
tool called Antidote. Antidote abstractly trains decision trees for an
intractably large space of possible poisoned datasets. Due to the soundness of
our abstraction, Antidote can produce proofs that, for a given input, the
corresponding prediction would not have changed had the training set been
tampered with or not. We demonstrate the effectiveness of Antidote on a number
of popular datasets.

16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th