Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large-Margin Determinantal Point Processes   Determinantal point processes (DPPs) offer a powerful approach to modeling
diversity in many applications where the goal is to select a diverse subset. We
study the problem of learning the parameters (the kernel matrix) of a DPP from
labeled training data. We make two contributions. First, we show how to
reparameterize a DPP's kernel matrix with multiple kernel functions, thus
enhancing modeling flexibility. Second, we propose a novel parameter estimation
technique based on the principle of large margin separation. In contrast to the
state-of-the-art method of maximum likelihood estimation, our large-margin loss
function explicitly models errors in selecting the target subsets, and it can
be customized to trade off different types of errors (precision vs. recall).
Extensive empirical studies validate our contributions, including applications
on challenging document and video summarization, where flexibility in modeling
the kernel matrix and balancing different errors is indispensable.

35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph