author
Co-Founder
,
Snorkel AI
Professor @ Stanford University
Christopher (Chris) Ré is a professor in the department of computer science at Stanford University. He is in the Stanford AI Lab and is affiliated with the Statistical Machine Learning Group. His recent work is to understand how software and hardware systems will change as a result of machine learning along with a continuing, petulant drive to work on math problems. Research from his group has been incorporated into scientific and humanitarian efforts, such as the fight against human trafficking, along with widely used products from technology and enterprise companies including Google Ads, Gmail, YouTube, and Apple.
He has co-founded four companies based on his research into machine learning systems, SambaNova and Snorkel, along with two companies that are now part of Apple, Lattice (DeepDive) in 2017, and Inductiv (HoloClean) in 2020.
His research contributions have spanned database theory, database systems, and machine learning. His work has won the best paper or test-of-time awards at the premier venues in each area. He still can’t believe he won the MacArthur Foundation Fellowship.
The latest from Chris
Learning Dependency Structures for Weak Supervision Models
Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge. We focus on a robust PCAbased algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real-world tasks. Under certain conditions, we show that the amount of unlabeled data needed can scale sublinearly or even logarithmically with the number of sources m, improving over previous efforts that ignore the...
Research Paper
Learning Dependency Structures for Weak Supervision Models
Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge. We focus on a robust PCAbased algorithm for learning these dependency structures, establish improved theoretical recovery…
Interactive Programmatic Labeling for Weak Supervision
Demonstrating in synthetic and real-world experiments how two simple labeling function acquisition strategies outperform a random baseline.
Research Paper
Interactive Programmatic Labeling for Weak Supervision
Demonstrating in synthetic and real-world experiments how two simple labeling function acquisition strategies outperform a random baseline.
A Machine-Compiled Database of Genome-Wide Association Studies
Describing GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms.
Research Paper
A Machine-Compiled Database of Genome-Wide Association Studies
Describing GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms.
Training Classifiers with Natural Language Explanations
Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5–100× faster by providing explanations instead of just labels. Furthermore, given...
Research Paper
Training Classifiers with Natural Language Explanations
Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount…
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Presenting Snorkel MeTal, an end-to-end system for multi-task learning.
Research Paper
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Presenting Snorkel MeTal, an end-to-end system for multi-task learning.
Fonduer: Knowledge Base Construction From Richly Formatted Data
Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.
Research Paper
Fonduer: Knowledge Base Construction From Richly Formatted Data
Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Research Paper
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Research Paper
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Learning the Structure of Generative Models Without Labeled Data
Proposing a structure estimation method that is 100x faster than a maximum likelihood approach for training data.
Research Paper
Learning the Structure of Generative Models Without Labeled Data
Proposing a structure estimation method that is 100x faster than a maximum likelihood approach for training data.
