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Found 6 papers, 0 papers with code
Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
In two-time-scale stochastic approximation (SA), two iterates are updated at different rates, governed by distinct step sizes, with each update influencing the other.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
In two-time-scale stochastic approximation (SA), two iterates are updated at varying speeds using different step sizes, with each update influencing the other.
Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach
Additionally, we propose the Debiased LPSA (DLPSA) as a practical application of our jump diffusion approximation result.
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
This construction is friendly to the analysis of PIFO algorithms.
DIPPA: An improved Method for Bilinear Saddle Point Problems
This paper studies bilinear saddle point problems $\min_{\bf{x}} \max_{\bf{y}} g(\bf{x}) + \bf{x}^{\top} \bf{A} \bf{y} - h(\bf{y})$, where the functions $g, h$ are smooth and strongly-convex.
