Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Doubly-stochastic mining for heterogeneous retrieval   Modern retrieval problems are characterised by training sets with potentially
billions of labels, and heterogeneous data distributions across subpopulations
(e.g., users of a retrieval system may be from different countries), each of
which poses a challenge. The first challenge concerns scalability: with a large
number of labels, standard losses are difficult to optimise even on a single
example. The second challenge concerns uniformity: one ideally wants good
performance on each subpopulation. While several solutions have been proposed
to address the first challenge, the second challenge has received relatively
less attention. In this paper, we propose doubly-stochastic mining (S2M ), a
stochastic optimization technique that addresses both challenges. In each
iteration of S2M, we compute a per-example loss based on a subset of hardest
labels, and then compute the minibatch loss based on the hardest examples. We
show theoretically and empirically that by focusing on the hardest examples,
S2M ensures that all data subpopulations are modelled well.

8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org