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Found 10 papers, 0 papers with code
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
To address the problem of lacking gradient in many applications, we propose two new stochastic zeroth-order Frank-Wolfe algorithms and theoretically proved that they have a faster convergence rate than existing methods for non-convex problems.
On the Convergence of Momentum-Based Algorithms for Federated Stochastic Bilevel Optimization Problems
In this paper, we studied the federated stochastic bilevel optimization problem.
Fast Training Method for Stochastic Compositional Optimization Problems
The stochastic compositional optimization problem covers a wide range of machine learning models, such as sparse additive models and model-agnostic meta-learning.
Accelerated Gradient-Free Method for Heavily Constrained Nonconvex Optimization
In this paper, to solve the nonconvex problem with a large number of white/black-box constraints, we proposed a doubly stochastic zeroth-order gradient method (DSZOG).
Fast Training Method for Stochastic Compositional Optimization Problems
The stochastic compositional optimization problem covers a wide range of machine learning models, such as sparse additive models and model-agnostic meta-learning.
Delay-Tolerant Local SGD for Efficient Distributed Training
The heavy communication for model synchronization is a major bottleneck for scaling up the distributed deep neural network training to many workers.
Adaptive Serverless Learning
To the best of our knowledge, this is the first adaptive decentralized training approach.
Periodic Stochastic Gradient Descent with Momentum for Decentralized Training
The condition for achieving the linear speedup is also provided for this variant.
Deep Relational Factorization Machines
Factorization Machines (FMs) is an important supervised learning approach due to its unique ability to capture feature interactions when dealing with high-dimensional sparse data.
Multi-View Subspace Clustering
In this paper, we propose a novel multi-view subspace clustering method.
