no code implementations • 22 Sep 2022 • Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski, Edward Lee, Anthony 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
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
no code implementations • 31 Mar 2022 • Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
Specifically, we establish that the worst-case error rate of this problem is $\Theta(n / m T)$ whenever $m \gtrsim n$.
1 code implementation • NeurIPS 2021 • Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
In this paper, we propose automatic implicit differentiation, an efficient and modular approach for implicit differentiation of optimization problems.
no code implementations • 20 May 2021 • Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul
We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition.
no code implementations • 24 May 2019 • Vitaly Feldman, Roy Frostig, Moritz Hardt
We show a new upper bound of $\tilde O(\max\{\sqrt{k\log(n)/(mn)}, k/n\})$ on the worst-case bias that any attack can achieve in a prediction problem with $m$ classes.
no code implementations • 8 Nov 2018 • Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl
Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes.
no code implementations • 22 Mar 2017 • Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer
We describe and analyze a simple random feature scheme (RFS) from prescribed compositional kernels.
1 code implementation • 10 Aug 2016 • Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation.
no code implementations • 22 Feb 2016 • Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford
To achieve our results, we first observe that ridge regression can be used to obtain a "smooth projection" onto the top principal components.
no code implementations • NeurIPS 2016 • Amit Daniely, Roy Frostig, Yoram Singer
We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning.
no code implementations • 24 Jun 2015 • Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
We develop a family of accelerated stochastic algorithms that minimize sums of convex functions.
no code implementations • 20 Dec 2014 • Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
In the absence of computational constraints, the minimizer of a sample average of observed data -- commonly referred to as either the empirical risk minimizer (ERM) or the $M$-estimator -- is widely regarded as the estimation strategy of choice due to its desirable statistical convergence properties.
1 code implementation • NeurIPS 2014 • Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning
We focus on the problem of maximum a posteriori (MAP) inference in Markov random fields with binary variables and pairwise interactions.
no code implementations • 21 Dec 2013 • Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning
We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field.