Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample-Efficient Safety Assurances using Conformal Prediction   When deploying machine learning models in high-stakes robotics applications,
the ability to detect unsafe situations is crucial. Early warning systems can
provide alerts when an unsafe situation is imminent (in the absence of
corrective action). To reliably improve safety, these warning systems should
have a provable false negative rate; i.e. of the situations that are unsafe,
fewer than $\epsilon$ will occur without an alert. In this work, we present a
framework that combines a statistical inference technique known as conformal
prediction with a simulator of robot/environment dynamics, in order to tune
warning systems to provably achieve an $\epsilon$ false negative rate using as
few as $1/\epsilon$ data points. We apply our framework to a driver warning
system and a robotic grasping application, and empirically demonstrate
guaranteed false negative rate while also observing low false detection
(positive) rate.

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The categories of this paper are: eess
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga