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Found 11 papers, 2 papers with code
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems
By leveraging the hidden smoothness of the problem, this paper presents a tighter analysis of ALSET for stochastic nested problems.
Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems
By leveraging the hidden smoothness of the problem, this paper presents a tighter analysis of ALSET for stochastic nested problems.
A Single-Timescale Method for Stochastic Bilevel Optimization
This paper develops a new optimization method for a class of stochastic bilevel problems that we term Single-Timescale stochAstic BiLevEl optimization (STABLE) method.
CADA: Communication-Adaptive Distributed Adam
This paper proposes an adaptive stochastic gradient descent method for distributed machine learning, which can be viewed as the communication-adaptive counterpart of the celebrated Adam method - justifying its name CADA.
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
In particular, we apply Adam to SCSC, and the exhibited rate of convergence matches that of the original Adam on non-compositional stochastic optimization.
VAFL: a Method of Vertical Asynchronous Federated Learning
Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients.
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
The new algorithms adaptively choose between fresh and stale stochastic gradients and have convergence rates comparable to the original SGD.
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
In this paper, we propose a general proximal incremental aggregated gradient algorithm, which contains various existing algorithms including the basic incremental aggregated gradient method.
Markov Chain Block Coordinate Descent
random and cyclic selections are either infeasible or very expensive.
On Markov Chain Gradient Descent
This paper studies Markov chain gradient descent, a variant of stochastic gradient descent where the random samples are taken on the trajectory of a Markov chain.
Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers
If no sufficient decrease is found, the current point is called an approximate $R$-local minimizer.
