Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Knowledge Uncertainty and Probabilistic Parameter Constraints   Incorporating domain knowledge into the modeling process is an effective way
to improve learning accuracy. However, as it is provided by humans, domain
knowledge can only be specified with some degree of uncertainty. We propose to
explicitly model such uncertainty through probabilistic constraints over the
parameter space. In contrast to hard parameter constraints, our approach is
effective also when the domain knowledge is inaccurate and generally results in
superior modeling accuracy. We focus on generative and conditional modeling
where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate
the framework with experiments on both synthetic and real-world data.

29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph