Neural networks under SGD

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Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Coherent Risk Measures: Spectral Approach

Explores coherent risk measures and the spectral approach to risk aversion, covering VaR, ES, subadditivity, convexity, and the creation of new risk measures.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Generalization in Deep Learning

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Aggregate Risk: Coherent Risk Measures

Explores the importance of aggregate risk and coherent risk measures, including their interpretation and implications.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Understanding Learning Dynamics of Neural Networks

Explores neural network learning dynamics, covering optimization, interference, and continual learning challenges.

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

Structures in Non-Convex Optimization

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.