Search Results for author: Nastaran Okati

Found 6 papers, 5 papers with code

On the Within-Group Fairness of Screening Classifiers

1 code implementation31 Jan 2023 Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez

Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes.


Improving Expert Predictions with Conformal Prediction

1 code implementation28 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.

Conformal Prediction

Differentiable Learning Under Triage

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.

Classification Under Human Assistance

1 code implementation21 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.

Classification General Classification +1

Regression Under Human Assistance

1 code implementation6 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.

BIG-bench Machine Learning Medical Diagnosis +1

Computational Approaches for Stochastic Shortest Path on Succinct MDPs

no code implementations24 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.

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