Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Provable Guarantees against Data Poisoning Using Self-Expansion and
  Compatibility   A recent line of work has shown that deep networks are highly susceptible to
backdoor data poisoning attacks. Specifically, by injecting a small amount of
malicious data into the training distribution, an adversary gains the ability
to control the model's behavior during inference. In this work, we propose an
iterative training procedure for removing poisoned data from the training set.
Our approach consists of two steps. We first train an ensemble of weak learners
to automatically discover distinct subpopulations in the training set. We then
leverage a boosting framework to recover the clean data. Empirically, our
method successfully defends against several state-of-the-art backdoor attacks,
including both clean and dirty label attacks. We also present results from an
independent third-party evaluation including a recent \textit{adaptive}
poisoning adversary. The results indicate our approach is competitive with
existing defenses against backdoor attacks on deep neural networks, and
significantly outperforms the state-of-the-art in several scenarios.

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The categories of this paper are: physics
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio