Asia
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Nikolas Ioannou, Celestine Mendler-Dünner, Thomas Parnell
Neural Information Processing Systems http://nips.cc/
Parameter-free Clipped Gradient Descent Meets Polyak Y uki T akezawa
Gradient descent and its variants are de facto standard algorithms for training machine learning models. As gradient descent is sensitive to its hyperparame-ters, we need to tune the hyperparameters carefully using a grid search.
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data Ziqing Fan
However, they cause another problem: space waste for personal tasks.
Temporally Consistent Atmospheric Turbulence Mitigation with Neural Representations
Atmospheric turbulence, caused by random fluctuations in the atmosphere's refractive index, introduces complex spatio-temporal distortions in imagery captured
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Ehsan Amid, Manfred K. K. Warmuth, Rohan Anil, Tomer Koren
Neural Information Processing Systems http://nips.cc/
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Neural Information Processing Systems http://nips.cc/
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
Due to the non-convex nature of such joint estimators, the theoretical justification of their efficiency is typically challenging.