Search Results for author: Shahin Jabbari

Found 12 papers, 2 papers with code

Improving Fairness in Adaptive Social Exergames via Shapley Bandits

no code implementations18 Feb 2023 Robert C. Gray, Jennifer Villareale, Thomas B. Fox, Diane H. Dallal, Santiago Ontañón, Danielle Arigo, Shahin Jabbari, Jichen Zhu

Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.

Fairness Multi-Armed Bandits

TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

1 code implementation1 Nov 2022 Vivek Khimani, Shahin Jabbari

With the increased legislation around data privacy, federated learning (FL) has emerged as a promising technique that allows the clients (end-user) to collaboratively train deep learning (DL) models without transferring and storing the data in a centralized, third-party server.

Federated Learning

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

no code implementations3 Feb 2022 Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju

To this end, we first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding.

BIG-bench Machine Learning

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

no code implementations21 Feb 2021 Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner.

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

no code implementations7 Jan 2021 Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe

However, given the limited number of health workers, only a small subset of the population can be visited in any given time period.

Combinatorial Optimization reinforcement-learning +1

Fair Influence Maximization: A Welfare Optimization Approach

no code implementations14 Jun 2020 Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe

Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.

Fairness Management

Equilibrium Characterization for Data Acquisition Games

no code implementations22 May 2019 Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael Kearns, Zachary Schutzman

We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data.

Fair Algorithms for Learning in Allocation Problems

no code implementations30 Aug 2018 Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman

We formalize this fairness notion for allocation problems and investigate its algorithmic consequences.


A Convex Framework for Fair Regression

1 code implementation7 Jun 2017 Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth

We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.

Fairness regression

Fairness in Criminal Justice Risk Assessments: The State of the Art

no code implementations27 Mar 2017 Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth

Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments.


Fairness in Reinforcement Learning

no code implementations ICML 2017 Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards.

Fairness reinforcement-learning +1

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs

no code implementations NeurIPS 2016 Shahin Jabbari, Ryan Rogers, Aaron Roth, Zhiwei Steven Wu

This models the problem of predicting the behavior of a rational agent whose goals are known, but whose resources are unknown.

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