16 citations · 26 across the 2 of their papers we have counts for
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cs.LG2019★ 16 cited
Distributionally Robust Optimization and Generalization in Kernel Methods
Matthew Staib, Stefanie Jegelka
Distributionally robust optimization (DRO) has attracted attention in machine learning due to its connections to regularization, generalization, and robustness. Existing work has c…
cs.LG2019
Escaping Saddle Points with Adaptive Gradient Methods
Matthew Staib, Sashank J. Reddi, Satyen Kale +2
Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood. In this paper, we seek a crisp, clean and precise characterization of their…
cs.LG2018
Distributionally Robust Submodular Maximization
Matthew Staib, Bryan Wilder, Stefanie Jegelka
Submodular functions have applications throughout machine learning, but in many settings, we do not have direct access to the underlying function . We focus on stochastic functi…