1.2k citations · 1.2k across the 4 of their papers we have counts for
4 papers
Overton: A Data System for Monitoring and Improving Machine-Learned Products
Christopher Ré, Feng Niu, Pallavi Gudipati +1
We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engin…
Scaling Inference for Markov Logic with a Task-Decomposition Approach
Feng Niu, Ce Zhang, Christopher Ré +1
Motivated by applications in large-scale knowledge base construction, we study the problem of scaling up a sophisticated statistical inference framework called Markov Logic Network…
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Feng Niu, Benjamin Recht, Christopher Re +1
Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently pro…
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
Feng Niu, Christopher Ré, AnHai Doan +1
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including…