Tag: level
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Parametric RDT approach to computational gap of symmetric binary perceptron
Parametric RDT approach to computational gap of symmetric binary perceptron arXiv:2601.10628v1 Announce Type: new Abstract: We study potential presence of statistical-computational gaps (SCG) in symmetric binary perceptrons (SBP) via a parametric utilization of emph{fully lifted random duality theory} (fl-RDT) [96]. A structural change from decreasingly to arbitrarily ordered $c$-sequence (a key fl-RDT parametric component) is…
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Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data
Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data arXiv:2512.12442v1 Announce Type: new Abstract: Almost all scientific data have uncertainties originating from different sources. Gaussian process regression (GPR) models are a natural way to model data with Gaussian-distributed uncertainties. GPR also has the benefit of reducing I/O bandwidth and storage requirements for large scientific simulations.…
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Efficient Penalty-Based Bilevel Methods: Improved Analysis, Novel Updates, and Flatness Condition
Efficient Penalty-Based Bilevel Methods: Improved Analysis, Novel Updates, and Flatness Condition arXiv:2511.16796v1 Announce Type: cross Abstract: Penalty-based methods have become popular for solving bilevel optimization (BLO) problems, thanks to their effective first-order nature. However, they often require inner-loop iterations to solve the lower-level (LL) problem and small outer-loop step sizes to handle the increased smoothness…
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GraphPPD: Posterior Predictive Modelling for Graph-Level Inference
GraphPPD: Posterior Predictive Modelling for Graph-Level Inference arXiv:2508.16995v1 Announce Type: new Abstract: Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitates the users’ understanding of the model’s confidence in its predictions. Along with the tremendously increasing…
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An $(epsilon,delta)$-accurate level set estimation with a stopping criterion
An $(epsilon,delta)$-accurate level set estimation with a stopping criterion arXiv:2503.20272v1 Announce Type: new Abstract: The level set estimation problem seeks to identify regions within a set of candidate points where an unknown and costly to evaluate function’s value exceeds a specified threshold, providing an efficient alternative to exhaustive evaluations of function values. Traditional methods often…
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Level Up Your Coding Skills with Python Threading
Level Up Your Coding Skills with Python Threading Learn how to use queues, daemon threads, and events in a Machine Learning project Continue reading on Towards Data Science ยป Marcello Politi Go to original source