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Found 5 papers, 1 papers with code
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging
Recently, an OGD algorithm emerged, employing gradient computation involving prior and most recent examples, a step that effectively reduces algorithmic complexity to $O(T)$, with $T$ being the number of received examples.
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory
Specifically, we establish a clear connection between the variance of online gradients and the regret, and construct online gradients using the most recent stratified samples with a limited buffer of size of $s$ representing all past data, which have a complexity of $O(sT)$ and employs $O(\sqrt{T}\log{T})$ random Fourier features for kernel approximation.
Computational Complexity of Sub-Linear Convergent Algorithms
One of these approaches is reducing the gradient variance through adaptive sampling to solve large-scale optimization's empirical risk minimization (ERM) problems.
Pairwise Learning via Stagewise Training in Proximal Setting
Recent research has, however, offered adaptive sample size training for smooth loss functions as a better strategy in terms of convergence and complexity, but without a comprehensive theoretical study.
Investigating a Baseline Of Self Supervised Learning Towards Reducing Labeling Costs For Image Classification
Results show that the pretext process in the self-supervised learning improves the accuracy around 15% in the downstream classification task when compared to the plain supervised learning.
