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Mert Pilanci, Stanford University Abstract: Since deep neural network training problems are inherently non-convex, their recent dramatic success largely relies on non-convex optimization heuristics and experimental findings. Despite significant advancements, the non-convex nature of neural network training poses two central challenges: first, understanding the underlying mechanisms that contribute to model performance, and second, achieving efficient […] |
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Ramya Korlakai Vinayak, University of Wisconsin–Madison Abstract: Machine learning (ML) algorithms are becoming ubiquitous in various application domains such as public health, genomics, psychology, and social sciences. In these domains, data is often obtained from populations that are diverse, e.g., varying demographics, phenotypes, preferences etc. Many ML algorithms focus on learning model parameters that work […] |
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