Minor cleaning
Gonzalez @ Smola et al -- Scalable Inference in Latent Variable Models

Implementation -- https://github.com/shravanmn/Yahoo_LDA

In this paper we present a scalable parallel framework for efficient inference
in latent variable models over streaming web-scale data. Our framework addresses
three key challenges: 1) synchronizing the global state which includes global
latent variables (e.g., cluster centers and dictionaries); 2) efficiently
storing and retrieving the large local state which includes the data-points and
their corresponding latent variables (e.g., cluster membership); and
3) sequentially incorporating streaming data (e.g., the news). We address these
challenges by introducing: 1) a novel delta-based aggregation system with a
bandwidth-efficient communication protocol; 2) schedule-aware out-of-core
storage; and 3) approximate forward sampling to rapidly incorporate new data.
We demonstrate state-of-the-art performance of our framework by easily tackling
datasets two orders of magnitude larger than those addressed by the current
state-of-the-art.