Trusted Machine Learning for Probabilistic Models

By Patrick Lincoln, Ashish Tiwari. 2016

Subfields: Safety, Robustness & Interpretability, Probabilistic & Bayesian Methods

How researchers see this paper

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

In Safety, Robustness & Interpretability

1. Xiaojin Zhu

In Probabilistic & Bayesian Methods

1. Adish Kumar Singla
2. Anna N. Rafferty
3. Sandra Zilles
4. Xiaojin Zhu
5. Shike Mei
6. Percy Liang
7. Pang Wei Koh
8. Scott Niekum

Citations in the corpus

• An Overview of Machine Teaching (2018, background): “This ranges from angelic (optimized, personalized education; improving cognitive models; fast classifier training; debugging machine learners [7, 17,46], etc.”