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Found 18 papers, 1 papers with code
Multi-View Subspace Clustering
In this paper, we propose a novel multi-view subspace clustering method.
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.
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.
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.
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.
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems
In this paper, we studied the federated bilevel optimization problem, which has widespread applications in machine learning.
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network
Bilevel optimization has been applied to a wide variety of machine learning models, and numerous stochastic bilevel optimization algorithms have been developed in recent years.
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems
Minimax optimization problems have attracted significant attention in recent years due to their widespread application in numerous machine learning models.
Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems?
This motivates us to study decentralized minimax optimization algorithms for the nonconvex-nonconcave problem.
When Decentralized Optimization Meets Federated Learning
Federated learning is a new learning paradigm for extracting knowledge from distributed data.
Stochastic Multi-Level Compositional Optimization Algorithms over Networks with Level-Independent Convergence Rate
To the best of our knowledge, this is the first work that achieves the level-independent convergence rate under the decentralized setting.
Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization
In particular, our study shows that the standard gossip communication strategy cannot achieve linear speedup for decentralized compositional minimax problems due to the large consensus error about the inner-level function.
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Vision-language pre-training (VLP) models have shown vulnerability to adversarial examples in multimodal tasks.
On the Communication Complexity of Decentralized Bilevel Optimization
To the best of our knowledge, this is the first stochastic algorithm achieving these theoretical results under the heterogeneous setting.
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.
