22 citations · 78 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Edward Kim, Zach Jensen, Alexander van Grootel +8
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experime…
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…