  We present a method for automatically organizing and evaluating the quality
of different publishing venues in Computer Science. Since this method only
requires paper publication data as its input, we can demonstrate our method on
a large portion of the DBLP dataset, spanning 50 years, with millions of
authors and thousands of publishing venues. By formulating venue authorship as
a regression problem and targeting metrics of interest, we obtain venue scores
for every conference and journal in our dataset. The obtained scores can also
provide a per-year model of conference quality, showing how fields develop and
change over time. Additionally, these venue scores can be used to evaluate
individual academic authors and academic institutions. We show that using venue
scores to evaluate both authors and institutions produces quantitative measures
that are comparable to approaches using citations or peer assessment. In
contrast to many other existing evaluation metrics, our use of large-scale,
openly available data enables this approach to be repeatable and transparent.
  To help others build upon this work, all of our code and data is available at
https://github.com/leonidk/venue_scores
