Statistical Learning
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
While our presentation focuses on this finite-sum structure, most of our convergence results can easily be adapted to the general stochastic setting (see App. D).
Unfolding recurrence by Green's functions for optimized reservoir computing Sandra Nestler
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Both of these problems have also received attention recently.
f-Divergence Variational Inference
V ariational inference (VI) is a machine learning method that makes Bayesian inference computationally efficient and scalable to large datasets.
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Recent research has seen several advances relevant to black-box variational inference (VI), but the current state of automatic posterior inference is unclear.
Adaptive First-Order Methods Revisited: Convex Optimization without Lipschitz Requirements
We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth.
Adaptive First-Order Methods Revisited: Convex Optimization without Lipschitz Requirements
We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth.
Acknowledgments
Do the main claims made in the abstract and introduction accurately reflect the paper's