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Roy Frostig
Roy Frostig
Research scientist, Google DeepMind
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
JAX: composable transformations of Python + NumPy programs
J Bradbury, R Frostig, P Hawkins, MJ Johnson, C Leary, D Maclaurin, ...
URL http://github.com/google/jax, 2018
23292018
Semantic Parsing on Freebase from Question-Answer Pairs
J Berant, A Chou, R Frostig, P Liang
Empirical methods in natural language processing (EMNLP), 2013
21472013
Measuring the Effects of Data Parallelism on Neural Network Training
CJ Shallue, J Lee, J Antognini, J Sohl-Dickstein, R Frostig, GE Dahl
Journal of Machine Learning Research 20 (112), 1-49, 2019
4142019
Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity
A Daniely, R Frostig, Y Singer
Advances In Neural Information Processing Systems 29, 2016
3612016
Compiling machine learning programs via high-level tracing
R Frostig, MJ Johnson, C Leary
SysML, 2018
2782018
Efficient and modular implicit differentiation
M Blondel, Q Berthet, M Cuturi, R Frostig, S Hoyer, F Llinares-López, ...
Advances in Neural Information Processing Systems 35, 5230-5242, 2022
1972022
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
R Frostig, R Ge, SM Kakade, A Sidford
Proceedings of The 32nd International Conference on Machine Learning, 2540-2548, 2015
1682015
Competing with the empirical risk minimizer in a single pass
R Frostig, R Ge, SM Kakade, A Sidford
Conference on learning theory, 728-763, 2015
1262015
Principal component projection without principal component analysis
R Frostig, C Musco, C Musco, A Sidford
International Conference on Machine Learning, 2349-2357, 2016
382016
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
X Xiao, T Zhang, K Choromanski, E Lee, A Francis, J Varley, S Tu, ...
arXiv preprint arXiv:2209.10780, 2022
362022
The advantages of multiple classes for reducing overfitting from test set reuse
V Feldman, R Frostig, M Hardt
International Conference on Machine Learning, 1892-1900, 2019
342019
Learning from many trajectories
S Tu, R Frostig, M Soltanolkotabi
arXiv preprint arXiv:2203.17193, 2022
212022
Simple MAP inference via low-rank relaxations
R Frostig, S Wang, PS Liang, CD Manning
Advances in Neural Information Processing Systems 27, 2014
212014
You Only Linearize Once: Tangents Transpose to Gradients
A Radul, A Paszke, R Frostig, MJ Johnson, D Maclaurin
Proceedings of the ACM on Programming Languages 7 (POPL), 1246-1274, 2023
182023
Random Features for Compositional Kernels
A Daniely, R Frostig, V Gupta, Y Singer
arXiv preprint arXiv:1703.07872, 2017
152017
Estimation from Indirect Supervision with Linear Moments
A Raghunathan, R Frostig, J Duchi, P Liang
arXiv preprint arXiv:1608.03100, 2016
132016
Decomposing reverse-mode automatic differentiation
R Frostig, MJ Johnson, D Maclaurin, A Paszke, A Radul
arXiv preprint arXiv:2105.09469, 2021
122021
Relaxations for inference in restricted Boltzmann machines
SI Wang, R Frostig, P Liang, CD Manning
arXiv preprint arXiv:1312.6205, 2013
72013
Parallelism-preserving automatic differentiation for second-order array languages
A Paszke, MJ Johnson, R Frostig, D Maclaurin
Proceedings of the 9th ACM SIGPLAN International Workshop on Functional High …, 2021
52021
Open Problem: How fast can a multiclass test set be overfit?
V Feldman, R Frostig, M Hardt
Conference on Learning Theory, 3185-3189, 2019
52019
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