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Adam can converge without any modification on update rules
Ever since\citet {reddi2019convergence} pointed out the divergence issue of Adam, many
new variants have been designed to obtain convergence. However, vanilla Adam remains …
new variants have been designed to obtain convergence. However, vanilla Adam remains …
The power of adaptivity in sgd: Self-tuning step sizes with unbounded gradients and affine variance
We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic
gradient methods (SGD), where the step sizes change based on observed stochastic …
gradient methods (SGD), where the step sizes change based on observed stochastic …
Efficiency of federated learning and blockchain in preserving privacy and enhancing the performance of credit card fraud detection (CCFD) systems
Increasing global credit card usage has elevated it to a preferred payment method for daily
transactions, underscoring its significance in global financial cybersecurity. This paper …
transactions, underscoring its significance in global financial cybersecurity. This paper …
[HTML][HTML] Predictive patterns and market efficiency: A deep learning approach to financial time series forecasting
This study explores market efficiency and behavior by integrating key theories such as the
Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational …
Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational …
On the convergence of adam under non-uniform smoothness: Separability from sgdm and beyond
This paper aims to clearly distinguish between Stochastic Gradient Descent with Momentum
(SGDM) and Adam in terms of their convergence rates. We demonstrate that Adam achieves …
(SGDM) and Adam in terms of their convergence rates. We demonstrate that Adam achieves …
Shuffling momentum gradient algorithm for convex optimization
Abstract The Stochastic Gradient Method (SGD) and its stochastic variants have become
methods of choice for solving finite-sum optimization problems arising from machine …
methods of choice for solving finite-sum optimization problems arising from machine …
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
K Tang, W Liu, Y Zhang, X Chen - arxiv preprint arxiv:2305.17665, 2023 - arxiv.org
Stochastic gradient descent with momentum (SGDM) has been widely used in many
machine learning and statistical applications. Despite the observed empirical benefits of …
machine learning and statistical applications. Despite the observed empirical benefits of …
Revisit last-iterate convergence of mSGD under milder requirement on step size
Understanding convergence of SGD-based optimization algorithms can help deal with
enormous machine learning problems. To ensure last-iterate convergence of SGD and …
enormous machine learning problems. To ensure last-iterate convergence of SGD and …
Revisiting the central limit theorems for the sgd-type methods
T Li, T **ao, G Yang - arxiv preprint arxiv:2207.11755, 2022 - arxiv.org
We revisited the central limit theorem (CLT) for stochastic gradient descent (SGD) type
methods, including the vanilla SGD, momentum SGD and Nesterov accelerated SGD …
methods, including the vanilla SGD, momentum SGD and Nesterov accelerated SGD …
On stationary point convergence of ppo-clip
Proximal policy optimization (PPO) has gained popularity in reinforcement learning (RL). Its
PPO-Clip variant is one the most frequently implemented algorithms and is one of the first-to …
PPO-Clip variant is one the most frequently implemented algorithms and is one of the first-to …