Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample Complexity of Uniform Convergence for Multicalibration   There is a growing interest in societal concerns in machine learning systems,
especially in fairness. Multicalibration gives a comprehensive methodology to
address group fairness. In this work, we address the multicalibration error and
decouple it from the prediction error. The importance of decoupling the
fairness metric (multicalibration) and the accuracy (prediction error) is due
to the inherent trade-off between the two, and the societal decision regarding
the "right tradeoff" (as imposed many times by regulators). Our work gives
sample complexity bounds for uniform convergence guarantees of multicalibration
error, which implies that regardless of the accuracy, we can guarantee that the
empirical and (true) multicalibration errors are close. We emphasize that our
results: (1) are more general than previous bounds, as they apply to both
agnostic and realizable settings, and do not rely on a specific type of
algorithm (such as deferentially private), (2) improve over previous
multicalibration sample complexity bounds and (3) implies uniform convergence
guarantees for the classical calibration error.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int