Optimization
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
Empirically, we verify the computational efficiency of our methods.
Convergence Analysis of ODE Models for Accelerated First-Order Methods via Positive Semidefinite Kernels
Consequently, our framework transforms the task of proving convergence rate into verifying the positive semidefiniteness of a specific integral kernel.
Finding Local Minima Efficiently in Decentralized Optimization
In this paper we study the second-order optimality of decentralized stochastic algorithm that escapes saddle point efficiently for nonconvex optimization problems.
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness
Consequently, standard online convex optimization algorithms do not directly apply.
