Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relative Loss Bounds for On-line Density Estimation with the Exponential
  Family of Distributions   We consider on-line density estimation with a parameterized density from the
exponential family. The on-line algorithm receives one example at a time and
maintains a parameter that is essentially an average of the past examples.
After receiving an example the algorithm incurs a loss which is the negative
log-likelihood of the example w.r.t. the past parameter of the algorithm. An
off-line algorithm can choose the best parameter based on all the examples. We
prove bounds on the additional total loss of the on-line algorithm over the
total loss of the off-line algorithm. These relative loss bounds hold for an
arbitrary sequence of examples. The goal is to design algorithms with the best
possible relative loss bounds. We use a certain divergence to derive and
analyze the algorithms. This divergence is a relative entropy between two
exponential distributions.

15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph