Probabilism, entropies and strictly proper scoring rules

By Jürgen Landes. 2015

26 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science, Probabilistic & Bayesian Methods, Information Theory, Philosophy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dustin Tran
2. Balaji Lakshminarayanan
3. Shreyas Padhy
4. Tania Bedrax-Weiss
5. Zi Lin
6. Jeremiah Zhe Liu
7. David M. Blei
8. Stefano Ermon

In History and Philosophy of Science

1. Zi Lin
2. Shreyas Padhy
3. Tania Bedrax-Weiss
4. Jeremiah Zhe Liu
5. Balaji Lakshminarayanan
6. Dustin Tran

In Probabilistic & Bayesian Methods

1. Balaji Lakshminarayanan
2. Zi Lin
3. Shreyas Padhy
4. Tania Bedrax-Weiss
5. Jeremiah Zhe Liu
6. Dustin Tran
7. David M. Blei
8. Rajesh Ranganath

In Information Theory

1. Zi Lin
2. Shreyas Padhy
3. Tania Bedrax-Weiss
4. Jeremiah Zhe Liu
5. Balaji Lakshminarayanan
6. Dustin Tran

In Philosophy

1. Zi Lin
2. Shreyas Padhy
3. Tania Bedrax-Weiss
4. Jeremiah Zhe Liu
5. Balaji Lakshminarayanan
6. Dustin Tran

Citations in the corpus

• Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, methodology): “…proper scoring rule) that measure a model’s performance in uncertainty calibration, handles the existence of unknown domain via a minimax formulation, and derives the solution by using a generalized version of maximum entropy theorem for Bergman scores (Grünwald and Dawid, 2004; Landes, 2015).”