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2020 – today
- 2024
[j3]Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi:
Learning from many trajectories. J. Mach. Learn. Res. 25: 216:1-216:109 (2024)- 2023
- [j2]Alexey Radul
, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin:
You Only Linearize Once: Tangents Transpose to Gradients. Proc. ACM Program. Lang. 7(POPL): 1246-1274 (2023) - 2022
- [c13]Xuesu Xiao, Tingnan Zhang, Krzysztof Marcin Choromanski, Tsang-Wei Edward Lee, Anthony G. Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani:
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRL 2022: 1708-1721 - [c12]Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. NeurIPS 2022 - [i14]Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi:
Learning from many trajectories. CoRR abs/2203.17193 (2022) - [i13]Alexey Radul, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin:
You Only Linearize Once: Tangents Transpose to Gradients. CoRR abs/2204.10923 (2022) - [i12]Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski, Tsang-Wei Edward Lee, Anthony G. Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani:
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRR abs/2209.10780 (2022) - 2021
- [c11]Adam Paszke, Matthew J. Johnson, Roy Frostig, Dougal Maclaurin:
Parallelism-preserving automatic differentiation for second-order array languages. FHPNC@ICFP 2021: 13-23 - [i11]Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul:
Decomposing reverse-mode automatic differentiation. CoRR abs/2105.09469 (2021) - [i10]Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. CoRR abs/2105.15183 (2021)
2010 – 2019
- 2019
- [j1]Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. J. Mach. Learn. Res. 20: 112:1-112:49 (2019) - [c10]Vitaly Feldman, Roy Frostig, Moritz Hardt:
Open Problem: How fast can a multiclass test set be overfit? COLT 2019: 3185-3189 - [c9]Vitaly Feldman, Roy Frostig, Moritz Hardt:
The advantages of multiple classes for reducing overfitting from test set reuse. ICML 2019: 1892-1900 - [i9]Vitaly Feldman, Roy Frostig, Moritz Hardt:
The advantages of multiple classes for reducing overfitting from test set reuse. CoRR abs/1905.10360 (2019) - 2018
- [i8]Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. CoRR abs/1811.03600 (2018) - 2017
- [b1]Roy Frostig:
Lightweight statistical learning : accelerating and avoiding empirical risk minimization. Stanford University, USA, 2017 - [i7]Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer:
Random Features for Compositional Kernels. CoRR abs/1703.07872 (2017) - 2016
- [c8]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. ICML 2016: 2349-2357 - [c7]Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. ICML 2016: 2568-2577 - [c6]Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. NIPS 2016: 2253-2261 - [i6]Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. CoRR abs/1602.05897 (2016) - [i5]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. CoRR abs/1602.06872 (2016) - [i4]Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. CoRR abs/1608.03100 (2016) - 2015
- [c5]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. COLT 2015: 728-763 - [c4]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. ICML 2015: 2540-2548 - [i3]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. CoRR abs/1506.07512 (2015) - 2014
- [c3]Roy Frostig, Sida Wang, Percy Liang, Christopher D. Manning:
Simple MAP Inference via Low-Rank Relaxations. NIPS 2014: 3077-3085 - [c2]Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning:
Relaxations for inference in restricted Boltzmann machines. ICLR (Workshop) 2014 - [i2]Roy Frostig, Sida I. Wang:
A sub-constant improvement in approximating the positive semidefinite Grothendieck problem. CoRR abs/1408.2270 (2014) - [i1]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. CoRR abs/1412.6606 (2014) - 2013
- [c1]Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang:
Semantic Parsing on Freebase from Question-Answer Pairs. EMNLP 2013: 1533-1544
Coauthor Index
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last updated on 2024-11-13 23:52 CET by the dblp team
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