Asia
Symbolic Regression with a Learned Concept Library
SR, uses zero-shot queries to a large language model (LLM) to discover and evolve abstract concepts occurring in known high-performing hypotheses.
Distributed Stochastic Optimization via Adaptive SGD
Ashok Cutkosky, Róbert Busa-Fekete
Neural Information Processing Systems http://nips.cc/
TheEffectsofRegularizationandDataAugmentation areClassDependent
Machine learning and deep learning aim at learning systems to solve as accurately as possible a given task at hand [LeCun et al., 1998, Bishop and Nasrabadi, 2006, Jordan and Mitchell, 2015].
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
Stephen Mussmann, Percy S. Liang
Neural Information Processing Systems http://nips.cc/
Decentralized sketching of low rank matrices
Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg
A fundamental structural model for data is that the data points lie close to an unknown subspace, meaning that the matrix created by concatenating the data vectors has low rank. We address a particular low-rank matrix recovery problem where we wish to recover a set of vectors from a low-dimensional subspace after they have been individually compressed (or "sketched").
