109 citations · 194 across the 5 of their papers we have counts for
5 papers
Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan He, Payal Bajaj +4
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sourc…
Accelerated Stochastic Power Iteration
Christopher De Sa, Bryan He, Ioannis Mitliagkas +2
Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires full-data pas…
Learning the Structure of Generative Models without Labeled Data
Stephen H. Bach, Bryan He, Alexander Ratner +1
Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale…
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Paroma Varma, Bryan He, Dan Iter +4
A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision so…
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Bryan He, Christopher De Sa, Ioannis Mitliagkas +1
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the va…