Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Mayee F. Chen, Ben Cohen-Wang, Steve Mussmann, Frederic Sala, Christopher Ré
International Conference on Artificial Intelligence and Statistics (AISTATS)
Multi-Resolution Weak Supervision for Sequential Data
Frederic Sala*, Paroma Varma*, Shiori Sagawa, Jason Fries, Daniel Y. Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré.
Neural Information Processing Systems (NeurIPS)
Weak supervision for machine learning models
ICML '20 |
NeurIPS '19 |
ICML '19 |
AAAI '19 |
Obtaining large amounts of labeled data is such a bottleneck that practitioners have increasingly turned to weaker forms of supervision. We study efficient algorithms for synthesizing labels from weak supervision sources with theoretical guarantees, an efficient way to learn the structure of a model of such sources, and new ways to tackle labeling data for large-scale video and other applications.
Geometry and structure of data
ACL '20 |
ICLR '19 |
ICML '18 |
blog 1 |
Modern ML methods require first embedding data into a continuous space-traditionally Euclidean space. However, the structure of data makes Euclidean space unsuitable for many types of data (like hierarchies!) My work shows that non-Euclidean spaces like hyperbolic space (and other manifolds!) are more suitable for embeddings and study the limits and tradeoffs of these techniques.
Efficient data synchronization and reconstruction
IT '19 |
IT '17 |
What is the least amount of information we must exchange to synchronize between two versions of a file, or to reconstruct a core piece of data from noisy samples? My work studies bounds and algorithms for these techniques.
Reliable data storage & next-gen memories
TCOM '17 |
TCOM '13 |
CL '14 |
SELSE '16 (best of) |
Modern memories offer speed and efficiency but suffer from physical limitations that lead to errors and corruption. New reliability and error-correcting techniques are critical to the future of these devices. My work develops new data representations, new coding techniques, and how to make algorithms more robust. I also study theoretical frameworks to evaluate broad ranges of ECCs.
- Top Reviewer NeurIPS, 2018, 2019
- Outstanding Ph.D. Dissertation Award, UCLA Department of Electrical Engineering (Signals & Systems Track) (2017)
- UCLA Dissertation Year Fellowship (2015-2016)
- Qualcomm Innovation Fellowship Finalist (2015)
- Edward K. Rice Outstanding Masters Student Award UCLA Henry Samueli School of Engineering & Applied Science (2013)
- Outstanding M.S. Thesis Award, UCLA Department of Electrical Engineering (Signals & Systems Track) (2013)
- National Science Foundation Graduate Research Fellowship (NSF GRFP) (2012-2015)