Machine learning is fundamentally changing the ways that people build and maintain software.
We are a CS research group at Stanford led by Professor Chris Ré interested in understanding those shifts and building the foundations for the next generation of machine learning systems. On the machine learning side, we’re fascinated by how we can learn from increasingly weak forms of supervision and understand the mathematical foundations of these techniques. On the systems side, we want to exploit our theoretical insights to help people more efficiently and effectively build, validate, and maintain machine learning models. And we are most excited when we can do both at the same time.
Check out our blog posts for an overview of our work and future directions we’re especially excited about!
Our group is supported by an amazing set of collaborators and sponsors, which we list here.
Posts
Zoology (Blogpost 0): Overview
Dec 11, 2023 · Simran Arora*, Michael Zhang*, Sabri Eyuboglu*.
Monarchs and Butterflies: Towards Sub-Quadratic Scaling in Model Dimension
Dec 11, 2023 · Dan Fu, with work from past & present students of Hazy Research.
Monarch Mixer: Revisiting BERT, Without Attention or MLPs
Jul 25, 2023 · Dan Fu*, Simran Arora*, Chris Ré
In The ChatGPT Era, Your Data is More Valuable Than Ever
Apr 20, 2023 · Chris Ré.
From Deep to Long Learning?
Mar 28, 2023 · Dan Fu, Michael Poli, Chris Ré.
Is AI Rare or Everywhere?
Mar 23, 2023 · Chris Ré.
Simple Long Convolutions for Sequence Modeling
Feb 15, 2023 · Dan Fu, Elliot Epstein, Eric Nguyen, Armin Thomas, Michael Zhang, Tri Dao, Atri Rudra, and Chris Ré.
AI's Linux Moment: An Open-Source AI Model Love Note
Jan 30, 2023 · Chris Ré.
Data Wrangling with Foundation Models
Jan 13, 2023 · Avanika Narayan, Ines Chami, Laurel Orr, and Chris Ré.
How Foundation Models Changed our Work
Nov 16, 2022 · Chris Ré
Foundation Models are Entering their Data-Centric Era
Oct 11, 2022 · Chris Ré and Simran Arora
Simplifying S4
Jun 21, 2022 · Chris Ré, Dan Fu, Karan Goel, Khaled Saab.
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
Apr 19, 2022 · Megan Leszczynski, Dan Fu, Mayee Chen, and Chris Ré.
An Introduction to Slice Discovery with Domino
Apr 2, 2022 · Sabri Eyuboglu, Maya Varma, Khaled Saab, Jared Dunnmon, James Zou and Chris Ré.
Structured State Spaces: A Brief Survey of Related Models
Jan 14, 2022 · Albert Gu, Karan Goel, Khaled Saab, and Chris Ré
Structured State Spaces for Sequence Modeling (S4)
Jan 14, 2022 · Albert Gu, Karan Goel, Khaled Saab, and Chris Ré
What Data Centric AI is Not
Sep 26, 2021 · Chris Ré and Simran Arora
The Road to Software 2.0 or Data-Centric AI
Jun 20, 2021 · Chris Ré
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
Nov 10, 2020 · Laurel Orr, Megan Leszczynski, Simran Arora, Neel Guha, Xiao Ling, Sen Wu, and Chris Ré
The Coming Wave of ML Systems
Oct 13, 2020 · Chris Ré, Piero Molino, Dan Fu, Karan Goel, Fiodar Kazhamakia, and Matei Zaharia
Weak Supervision for Science and Medicine: A Year in Review
Mar 2, 2020 · Jared Dunnmon and Chris Ré. Referencing work by other members of Hazy Research.
When Multi-Task Learning Works -- And When It Doesn’t
Mar 1, 2020 · Sen Wu, Hongyang Zhang and Chris Ré.
Towards Interactive Weak Supervision with FlyingSquid
Feb 28, 2020 · Dan Fu, Mayee Chen, Fred Sala, Sarah Hooper, Kayvon Fatahalian, and Chris Ré
Automating the Art of Data Augmentation
Part IV New Direction
Feb 26, 2020 · Karan Goel, Albert Gu, Sharon Li and Chris Ré
Automating the Art of Data Augmentation
Part III Theory
Feb 26, 2020 · Edited by Hongyang Zhang, Sharon Li and Chris Ré. Referencing work by many other members of Hazy Research.
Automating the Art of Data Augmentation
Part II Practical Methods
Automating the Art of Data Augmentation
Part I Overview
Feb 26, 2020 · Series edited by Sharon Li and Chris Ré. Referencing work by many other members of Hazy Research.
Into the Wild: Machine Learning In Non-Euclidean Spaces
Oct 10, 2019 · Fred Sala, Ines Chami, Adva Wolf, Albert Gu, Beliz Gunel and Chris Ré
Butterflies Are All You Need: A Universal Building Block for Structured Linear Maps
Jun 13, 2019 · Tri Dao, Albert Gu, Matthew Eichhorn, Megan Leszczynski, Nimit Sohoni, Amit Blonder, Atri Rudra, and Chris Ré
Learning Dependency Structures in Weak Supervision
Jun 12, 2019 · Fred Sala, Paroma Varma, Chris Ré
Massive Multi-Task Learning with Snorkel MeTaL: Bringing More Supervision to Bear
Mar 22, 2019 · Braden Hancock, Clara McCreery, Ines Chami, Vincent S. Chen, Sen Wu, Jared Dunnmon, Paroma Varma, Max Lam, and Chris Ré
Debugging Machine Learning - Reflections from DAWN Retreat
Sep 27, 2018 · Paroma Varma, Chris Ré, and other members of DAWN
Fonduer: Knowledge Base Construction from Richly Formatted Data
Mar 16, 2017 · Sen Wu, Luke Hsiao, and Chris Ré