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Found 9 papers, 2 papers with code
Shuffling Momentum Gradient Algorithm for Convex Optimization
The Stochastic Gradient Descent method (SGD) and its stochastic variants have become methods of choice for solving finite-sum optimization problems arising from machine learning and data science thanks to their ability to handle large-scale applications and big datasets.
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series
Foundation models have recently gained attention within the field of machine learning thanks to its efficiency in broad data processing.
Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach
Representation learning for time series has been an important research area for decades.
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values.
Finding Optimal Policy for Queueing Models: New Parameterization
In this work, we investigate the optimization aspects of the queueing model as a RL environment and provide insight to learn the optimal policy efficiently.
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
This rate is better than that of any other shuffling gradient methods in convex regime.
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution
How and under what assumptions is guaranteed convergence to a \textit{global} minimum possible?
New Perspective on the Global Convergence of Finite-Sum Optimization
How and under what assumptions is guaranteed convergence to a \textit{global} minimum possible?
SMG: A Shuffling Gradient-Based Method with Momentum
When the shuffling strategy is fixed, we develop another new algorithm that is similar to existing momentum methods, and prove the same convergence rates for this algorithm under the $L$-smoothness and bounded gradient assumptions.
