1 citations · 1 across the 2 of their papers we have counts for
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Calibrated Target Noise Recovers Curvature from the Gradients
Arash Jamshidi, Katsiaryna Haitsiukevich, Aristides Gionis +1
Estimating curvature information from gradients alone is a fundamental problem in machine learning. The challenge becomes particularly acute in settings where only aggregate mini-b…
Information Hidden in Gradients of Regression with Target Noise
Arash Jamshidi, Katsiaryna Haitsiukevich, Kai Puolamäki
Second-order information -- such as curvature or data covariance -- is critical for optimisation, diagnostics, and robustness. However, in many modern settings, only the gradients…
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich +3
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading…