A Synthetic Prediction Market for Estimating Confidence in Published Work

no code implementations • 23 Dec 2021 • Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmaan Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael McLaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, C. Lee Giles

Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress.

Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models

no code implementations • 8 Apr 2021 • Jian Wu, Rajal Nivargi, Sree Sai Teja Lanka, Arjun Manoj Menon, Sai Ajay Modukuri, Nishanth Nakshatri, Xin Wei, Zhuoer Wang, James Caverlee, Sarah M. Rajtmajer, C. Lee Giles

In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features.

BIG-bench Machine Learning

Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets

no code implementations • 5 Jan 2021 • Nishanth Nakshatri, Arjun Menon, C. Lee Giles, Sarah Rajtmajer, Christopher Griffin

We show that under certain assumptions on the underlying geometry, the resulting synthetic prediction market can be used to arbitrarily closely approximate a binary function defined on a set of input data.