Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for
  Machine Learning   Data Shapley has recently been proposed as a principled framework to quantify
the contribution of individual datum in machine learning. It can effectively
identify helpful or harmful data points for a learning algorithm. In this
paper, we propose Beta Shapley, which is a substantial generalization of Data
Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the
Shapley value, which is not critical for machine learning settings. Beta
Shapley unifies several popular data valuation methods and includes data
Shapley as a special case. Moreover, we prove that Beta Shapley has several
desirable statistical properties and propose efficient algorithms to estimate
it. We demonstrate that Beta Shapley outperforms state-of-the-art data
valuation methods on several downstream ML tasks such as: 1) detecting
mislabeled training data; 2) learning with subsamples; and 3) identifying
points whose addition or removal have the largest positive or negative impact
on the model.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con