Roy Frostig

scholar, arXiv

I'm a research scientist at Google Brain. My research develops computational tools and analysis core to statistical machine learning.

Percy Liang was my PhD advisor at Stanford, where I was part of the statistical machine learning group.

Research papers
Random features for compositional kernels.
Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer.
arXiv preprint arXiv:1703.07872, 2017.
bib - paper
Estimation from indirect supervision with linear moments.
Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang,
International Conference on Machine Learning (ICML), 2016.
bib - paper
Principal component projection without principal component analysis.
Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford.
International Conference on Machine Learning (ICML), 2016.
bib - paper
Toward deeper understanding of neural networks: the power of initialization and a dual view on expressivity.
Amit Daniely, Roy Frostig, Yoram Singer.
Advances in Neural Information Processing Systems (NIPS), 2016.
bib - paper
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization.
Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford.
International Conference on Machine Learning (ICML), 2015.
bib - paper
Competing with the empirical risk minimizer in a single pass.
Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford.
Conference on Learning Theory (COLT), 2015.
bib - paper
Simple MAP inference via low-rank relaxations.
Roy Frostig, Sida Wang, Percy Liang, Chris Manning.
Advances in Neural Information Processing Systems (NIPS), 2014.
bib - paper - experiments
Semantic parsing on Freebase from question-answer pairs.
Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang.
Empirical Methods in Natural Language Processing (EMNLP), 2013.
bib - paper - supplemental material - slides - project
Open-source involvements
Sempre is our semantic parsing toolkit.
Rust is a safe, concurrent systems programming language.