author
Co-Founder & CEO, Snorkel AI
Faculty, University of Washington
Alex Ratner is the co-founder and CEO at Snorkel AI, and an affiliate assistant professor of computer science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in computer science advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project. His research focused on data-centric AI, applying data management and statistical learning techniques to AI data development and curation.
The latest from Alex
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.
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.
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.
Swellshark: A Generative Model for Biomedical Named Entity Recognition Without Labeled Data
Introducing SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly.
Research Paper
Swellshark: A Generative Model for Biomedical Named Entity Recognition Without Labeled Data
Introducing SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly.
Snorkel: Rapid Training Data Creation With Weak Supervision
This paper presents a flexible interface layer to write labeling functions based on experience.
Research Paper
Snorkel: Rapid Training Data Creation With Weak Supervision
This paper presents a flexible interface layer to write labeling functions based on experience.
