Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Sampling for Min-Max Fairness   We propose simple active sampling and reweighting strategies for optimizing
min-max fairness that can be applied to any classification or regression model
that is learned via loss minimization. The key intuition behind our approach is
to use at each timestep a datapoint from the group that is worst off under the
current model for updating the model. The ease of implementation and the
generality of our robust formulation make it an attractive option for improving
model performance on badly performing groups. For convex learning problems,
such as linear or logistic regression, we provide a fine-grained analysis of
our strategy, proving its rate of convergence to a min-max fair solution.

12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol