Publications associées à Sparse convex optimization methods for machine learning

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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Sparse convex optimization methods for machine learning

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On the Generalization of Stochastic Gradient Descent with Momentum

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Aiming beyond slight increases in accuracy

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

The Virtue of Complexity in Return Prediction

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Nine recommendations for the governance of AI systems

On the Generalization of Stochastic Gradient Descent with Momentum

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Aiming beyond slight increases in accuracy

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

The Virtue of Complexity in Return Prediction

Nine recommendations for the governance of AI systems

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation