2 citations · 3 across the 4 of their papers we have counts for
4 papers
Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework
Olivier Binette, Jerome P. Reiter
Commonly, AI or machine learning (ML) models are evaluated on benchmark datasets. This practice supports innovative methodological research, but benchmark performance can be poorly…
How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
Olivier Binette, Youngsoo Baek, Siddharth Engineer +3
Entity resolution (record linkage, microclustering) systems are notoriously difficult to evaluate. Looking for a needle in a haystack, traditional evaluation methods use sophistica…
PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguation
Olivier Binette, Sarvo Madhavan, Jack Butler +3
We present PatentsView-Evaluation, a Python package that enables researchers to evaluate the performance of inventor name disambiguation systems such as PatentsView.org. The packag…
On the Reliability of Multiple Systems Estimation for the Quantification of Modern Slavery
Olivier Binette, Rebecca C. Steorts
The quantification of modern slavery has received increased attention recently as organizations have come together to produce global estimates, where multiple systems estimation (M…