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On the Generalization of Stochastic Gradient Descent with Momentum

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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On the Generalization of Stochastic Gradient Descent with Momentum

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Phenomenological theory of variational quantum ground-state preparation

Robust Collaborative Learning with Linear Gradient Overhead

Probabilistic methods for neural combinatorial optimization

Universal and adaptive methods for robust stochastic optimization

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Masked Training of Neural Networks with Partial Gradients

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

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust Collaborative Learning with Linear Gradient Overhead

On the Generalization of Stochastic Gradient Descent with Momentum

Probabilistic methods for neural combinatorial optimization

Universal and adaptive methods for robust stochastic optimization

Phenomenological theory of variational quantum ground-state preparation

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

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Masked Training of Neural Networks with Partial Gradients