39 citations · 175 across the 48 of their papers we have counts for
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cs.LG2019★ 39 cited
Orthogonal Gradient Descent for Continual Learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott +1
Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a co…
cs.LG2019★ 29 cited
Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
Navid Azizan, Sahin Lale, Babak Hassibi
Most modern learning problems are highly overparameterized, meaning that there are many more parameters than the number of training data points, and as a result, the training loss…
math.OC2019
A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality
Navid Azizan, Babak Hassibi
Stochastic mirror descent (SMD) is a fairly new family of algorithms that has recently found a wide range of applications in optimization, machine learning, and control. It can be…