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Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks
We study the convergence rate of first-order methods for rectangular matrix factorization, which is a canonical nonconvex optimization problem.
AMathematicalFrameworkforQuantifying TransferabilityinMulti-sourceTransferLearning
Therefore, forasource task withacomplexmodel orfewtraining samples, even though itis similar to the target task, the knowledge transferable from this source task can still be verylimited.
Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing
Neural Information Processing Systems http://nips.cc/
Average Individual Fairness: Algorithms, Generalization and Experiments
Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
Neural Information Processing Systems http://nips.cc/
db8e1af0cb3aca1ae2d0018624204529-Paper.pdf
Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources.
Geo-SIC: LearningDeformableGeometric ShapesinDeepImageClassifiers
We demonstrate the effectiveness of our method on both simulated 2D images andreal3Dbrain magnetic resonance (MR) images.