Tom J. Viering

Orcid: 0000-0002-7337-8624

According to our database1, Tom J. Viering authored at least 15 papers between 2017 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2025
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought.
CoRR, May, 2025

Learning Learning Curves.
Pattern Anal. Appl., March, 2025

2024
The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization.
Neurocomputing, 2024

2023
The Shape of Learning Curves: A Review.
IEEE Trans. Pattern Anal. Mach. Intell., June, 2023

On Safety in Machine Learning.
PhD thesis, 2023

2022
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance.
CoRR, 2022

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2020
A Brief Prehistory of Double Descent.
CoRR, 2020

Making Learners (More) Monotone.
Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.
Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

2019
Nuclear discrepancy for single-shot batch active learning.
Mach. Learn., 2019

How to Manipulate CNNs to Make Them Lie: the GradCAM Case.
CoRR, 2019

Minimizers of the Empirical Risk and Risk Monotonicity.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Open Problem: Monotonicity of Learning.
Proceedings of the Conference on Learning Theory, 2019

2017
Nuclear Discrepancy for Active Learning.
CoRR, 2017


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