67 citations · 110 across the 8 of their papers we have counts for
5 papers · 1 filter
Learning from many trajectories
Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
We initiate a study of supervised learning from many independent sequences ("trajectories") of non-independent covariates, reflecting tasks in sequence modeling, control, and reinf…
Efficient and Modular Implicit Differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi +5
Automatic differentiation (autodiff) has revolutionized machine learning. It allows to express complex computations by composing elementary ones in creative ways and removes the bu…
The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman, Roy Frostig, Moritz Hardt
Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks…
Measuring the Effects of Data Parallelism on Neural Network Training
Christopher J. Shallue, Jaehoon Lee, Joseph Antognini +3
Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation har…
Random Features for Compositional Kernels
Amit Daniely, Roy Frostig, Vineet Gupta +1
We describe and analyze a simple random feature scheme (RFS) from prescribed compositional kernels. The compositional kernels we use are inspired by the structure of convolutional…