1 code implementation • 31 Jan 2023 • Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes.
1 code implementation • 28 Jan 2022 • Eleni Straitouri, Lequn Wang, Nastaran Okati, Manuel Gomez Rodriguez
In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance.
2 code implementations • NeurIPS 2021 • Nastaran Okati, Abir De, Manuel Gomez-Rodriguez
However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood.
1 code implementation • 21 Jun 2020 • Abir De, Nastaran Okati, Ali Zarezade, Manuel Gomez-Rodriguez
Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.
1 code implementation • 6 Sep 2019 • Abir De, Nastaran Okati, Paramita Koley, Niloy Ganguly, Manuel Gomez-Rodriguez
In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels.
no code implementations • 24 Apr 2018 • Krishnendu Chatterjee, Hongfei Fu, Amir Kafshdar Goharshady, Nastaran Okati
We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables.