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Peter Nickl

3 accepted papers

2024

Variational Learning is Effective for Large Deep Networks

ICML 2024spotlight

We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variational Online Newton (IVON) consistently matches or outperforms Adam for training large networks such as GPT-2 and ResNets…

2023

The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data

NeurIPS 2023poster

Understanding model’s sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory-Perturbation Equation (MPE) which relates model's sensitivity to perturbation in its training data. Derived using Bayes…

2021

A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning

ICRA 2021poster

Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities in the data. Classical regressors usually fulfill only…

Cited by 5SourcecodeScholar