Roy Frostig - RankMe

31 papers A* 8Misc 1Journal 17Unranked 4

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Learning from many trajectories. J. Mach. Learn. Res. Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
2023	J	`jnl`	You Only Linearize Once: Tangents Transpose to Gradients. Proc. ACM Program. Lang. Alexey Radul, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin
2022	A*	`conf`	Efficient and Modular Implicit Differentiation. NeurIPS Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
2022	Misc	`conf`	Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRL 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
2022	J	`jnl`	Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation. CoRR 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
2022	J	`jnl`	Learning from many trajectories. CoRR Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
2022	J	`jnl`	You Only Linearize Once: Tangents Transpose to Gradients. CoRR Alexey Radul, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin
2021	J	`jnl`	Decomposing reverse-mode automatic differentiation. CoRR Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul
2021	J	`jnl`	Efficient and Modular Implicit Differentiation. CoRR Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
2021	—	`conf`	Parallelism-preserving automatic differentiation for second-order array languages. FHPNC@ICFP Adam Paszke, Matthew J. Johnson, Roy Frostig, Dougal Maclaurin
2019	J	`jnl`	Measuring the Effects of Data Parallelism on Neural Network Training. J. Mach. Learn. Res. Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl
2019	A*	`conf`	Open Problem: How fast can a multiclass test set be overfit? COLT Vitaly Feldman, Roy Frostig, Moritz Hardt
2019	A*	`conf`	The advantages of multiple classes for reducing overfitting from test set reuse. ICML Vitaly Feldman, Roy Frostig, Moritz Hardt
2019	J	`jnl`	The advantages of multiple classes for reducing overfitting from test set reuse. CoRR Vitaly Feldman, Roy Frostig, Moritz Hardt
2018	J	`jnl`	Measuring the Effects of Data Parallelism on Neural Network Training. CoRR Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl
2017	—	`—`	Lightweight statistical learning : accelerating and avoiding empirical risk minimization. Roy Frostig
2017	J	`jnl`	Random Features for Compositional Kernels. CoRR Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer
2016	A*	`conf`	Estimation from Indirect Supervision with Linear Moments. ICML Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
2016	J	`jnl`	Estimation from Indirect Supervision with Linear Moments. CoRR Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
2016	A*	`conf`	Principal Component Projection Without Principal Component Analysis. ICML Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford
2016	J	`jnl`	Principal Component Projection Without Principal Component Analysis. CoRR Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford
2016	—	`conf`	Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. NIPS Amit Daniely, Roy Frostig, Yoram Singer
2016	J	`jnl`	Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. CoRR Amit Daniely, Roy Frostig, Yoram Singer
2015	A*	`conf`	Competing with the Empirical Risk Minimizer in a Single Pass. COLT Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2015	A*	`conf`	Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. ICML Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2015	J	`jnl`	Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. CoRR Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2014	J	`jnl`	A sub-constant improvement in approximating the positive semidefinite Grothendieck problem. CoRR Roy Frostig, Sida I. Wang
2014	J	`jnl`	Competing with the Empirical Risk Minimizer in a Single Pass. CoRR Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2014	—	`conf`	Relaxations for inference in restricted Boltzmann machines. ICLR (Workshop) Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning
2014	—	`conf`	Simple MAP Inference via Low-Rank Relaxations. NIPS Roy Frostig, Sida Wang, Percy Liang, Christopher D. Manning
2013	A*	`conf`	Semantic Parsing on Freebase from Question-Answer Pairs. EMNLP Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang