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Found 8 papers, 1 papers with code
On the Error-Propagation of Inexact Deflation for Principal Component Analysis
Principal Component Analysis (PCA) is a popular tool in data analysis, especially when the data is high-dimensional.
Adaptive Federated Learning with Auto-Tuned Clients
Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data.
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis
The extragradient method has gained popularity due to its robust convergence properties for differentiable games.
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines.
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Stochastic gradient descent with momentum (SGDM) is the dominant algorithm in many optimization scenarios, including convex optimization instances and non-convex neural network training.
How much pre-training is enough to discover a good subnetwork?
Aiming to mathematically analyze the amount of dense network pre-training needed for a pruned network to perform well, we discover a simple theoretical bound in the number of gradient descent pre-training iterations on a two-layer, fully-connected network, beyond which pruning via greedy forward selection [61] yields a subnetwork that achieves good training error.
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.
Fast quantum state reconstruction via accelerated non-convex programming
Despite being a non-convex method, \texttt{MiFGD} converges \emph{provably} close to the true density matrix at an accelerated linear rate, in the absence of experimental and statistical noise, and under common assumptions.
