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Found 6 papers, 3 papers with code
SGD with Clipping is Secretly Estimating the Median Gradient
We then derive iterative methods based on the stochastic proximal point method for computing the geometric median and generalizations thereof.
SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms
Adaptive optimization methods are widely recognized as among the most popular approaches for training Deep Neural Networks (DNNs).
Variational Inference with Gaussian Score Matching
We study how GSM-VI behaves as a function of the problem dimensionality, the condition number of the target covariance matrix (when the target is Gaussian), and the degree of mismatch between the approximating and exact posterior distribution.
Provable convergence guarantees for black-box variational inference
Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds.
RSN: Randomized Subspace Newton
We develop a randomized Newton method capable of solving learning problems with huge dimensional feature spaces, which is a common setting in applications such as medical imaging, genomics and seismology.
Adaptive Sketch-and-Project Methods for Solving Linear Systems
We present new adaptive sampling rules for the sketch-and-project method for solving linear systems.
Numerical Analysis Numerical Analysis 15A06, 15B52, 65F10, 68W20, 65N75, 65Y20, 68Q25, 68W40, 90C20
