  We present the results of a machine-learning (ML) based search for new R
Coronae Borealis (RCB) stars and DY Persei-like stars (DYPers) in the Galaxy
using cataloged light curves from the All-Sky Automated Survey (ASAS) Catalog
of Variable Stars (ACVS). RCB stars - a rare class of hydrogen-deficient
carbon-rich supergiants - are of great interest owing to the insights they can
provide on the late stages of stellar evolution. DYPers are possibly the
low-temperature, low-luminosity analogs to the RCB phenomenon, though
additional examples are needed to fully establish this connection. While RCB
stars and DYPers are traditionally identified by epochs of extreme dimming that
occur without regularity, the ML search framework more fully captures the
richness and diversity of their photometric behavior. We demonstrate that our
ML method can use newly discovered RCB stars to identify additional candidates
within the same data set. Our search yields 15 candidates that we consider
likely RCB stars/DYPers: new spectroscopic observations confirm that four of
these candidates are RCB stars and four are DYPers. Our discovery of four new
DYPers increases the number of known Galactic DYPers from two to six;
noteworthy is that one of the new DYPers has a measured parallax and is m ~ 7
mag, making it the brightest known DYPer to date. Future observations of these
new DYPers should prove instrumental in establishing the RCB connection. We
consider these results, derived from a machine-learned probabilistic
classification catalog, as an important proof-of-concept for the efficient
discovery of rare sources with time-domain surveys.
