23 citations · 52 across the 3 of their papers we have counts for
10 papers
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Yiding Jiang, Pierre Foret, Scott Yak +7
Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of pro…
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
Edward Moroshko, Suriya Gunasekar, Blake Woodworth +3
We provide a detailed asymptotic study of gradient flow trajectories and their implicit optimization bias when minimizing the exponential loss over "diagonal linear networks". This…
Kernel and Rich Regimes in Overparametrized Models
Blake Woodworth, Suriya Gunasekar, Jason D. Lee +5
A recent line of work studies overparametrized neural networks in the "kernel regime," i.e. when the network behaves during training as a kernelized linear predictor, and thus trai…
Kernel and Rich Regimes in Overparametrized Models
Blake Woodworth, Suriya Gunasekar, Pedro Savarese +5
A recent line of work studies overparametrized neural networks in the "kernel regime," i.e. when the network behaves during training as a kernelized linear predictor, and thus trai…
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee +2
With an eye toward understanding complexity control in deep learning, we study how infinitesimal regularization or gradient descent optimization lead to margin maximizing solutions…
On preserving non-discrimination when combining expert advice
Avrim Blum, Suriya Gunasekar, Thodoris Lykouris +1
We study the interplay between sequential decision making and avoiding discrimination against protected groups, when examples arrive online and do not follow distributional assumpt…