Optimization in ML
Optimization is a key component of machine learning. In this series we review recent developments allowing to train larger models, faster and more reliably.

Research feed

Seminar

Second-Order Information and Applications

Kristof Schröder will talk about second-order information and its applications in machine learning.

Kristof Schröder
Optimization in ML
Pill

Learning-rate-free learning by D-Adaptation

Fine-tuning the learning rate (lr) in the training of neural networks is crucial for their performance, and often requires a costly search …

Samuel Burbulla
Optimization in ML

Other series in Advances and fundamentals in ML

Simulation and AI

AI techniques are fundamentally transforming the field of simulation by combining physics-based modeling with data-driven machine learning.

Advances and Fundamentals in ML

Reinforcement Learning

Recent and popular advances in Reinforcement Learning are known to be data-hungry. Attempts to handle this deficit include developing …

Advances and Fundamentals in ML

Diffusion Models

Diffusion models (DM) have become the state of the art for sample quality in generative modelling. They work by sequentially corrupting …

Advances and Fundamentals in ML

Geometric deep learning

Specialized deep learning architectures exploit the intrinsic regularities arising from the underlying structure of the physical world. …

Advances and Fundamentals in ML
Check all of our work