Shahin Jabbari

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 33

Top Topics

Reinforcement Learning

Influence Maximization

Mechanical Turk

Minimally Supervised

Top Venues

Publications

Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. Trans. Mach. Learn. Res. 2024 (2024)
David Gefen, Shahin Jabbari, Rezvaneh (Shadi) Rezapour, Aria Pessianzadeh, Kshitij Kayastha, Hilde Van den Bulck
The Evolving Meaning of Trust and Risk in Reddit Discourse about ChatGPT. AMCIS (2024)
Robert C. Gray, Jennifer Villareale, Thomas B. Fox, Diane H. Dallal, Santiago Ontañón, Danielle Arigo, Shahin Jabbari, Jichen Zhu
Improving Fairness in Adaptive Social Exergames via Shapley Bandits. CoRR (2023)
Robert C. Gray, Jennifer Villareale, Thomas Boyd Fox, Diane H. Dallal, Santiago Ontañón, Danielle Arigo, Shahin Jabbari, Jichen Zhu
Improving Fairness in Adaptive Social Exergames via Shapley Bandits. IUI (2023)
Palvi Aggarwal, Omkar Thakoor, Shahin Jabbari, Edward A. Cranford, Christian Lebiere, Milind Tambe, Cleotilde Gonzalez
Designing effective masking strategies for cyberdefense through human experimentation and cognitive models. Comput. Secur. 117 (2022)
Zun Li, Feiran Jia, Aditya Mate, Shahin Jabbari, Mithun Chakraborty, Milind Tambe, Yevgeniy Vorobeychik
Solving structured hierarchical games using differential backward induction. UAI (2022)
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. CoRR (2022)
Vivek Khimani, Shahin Jabbari
TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments. CoRR (2022)
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe
Fair Influence Maximization: a Welfare Optimization Approach. AAAI (2021)
Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach. AAMAS (2021)
Zun Li, Feiran Jia, Aditya Mate, Shahin Jabbari, Mithun Chakraborty, Milind Tambe, Yevgeniy Vorobeychik
Solving Structured Hierarchical Games Using Differential Backward Induction. CoRR (2021)
Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach. CoRR (2021)
Feiran Jia, Aditya Mate, Zun Li, Shahin Jabbari, Mithun Chakraborty, Milind Tambe, Michael P. Wellman, Yevgeniy Vorobeychik
A Game-Theoretic Approach for Hierarchical Policy-Making. CoRR (2021)
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. ICML (2021)
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. CoRR (2021)
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, Milind Tambe
Fair Influence Maximization: A Welfare Optimization Approach. CoRR (2020)
Omkar Thakoor, Shahin Jabbari, Palvi Aggarwal, Cleotilde Gonzalez, Milind Tambe, Phebe Vayanos
Exploiting Bounded Rationality in Risk-Based Cyber Camouflage Games. GameSec (2020)
Ankit Bhardwaj, Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe, Rahul Panicker, Alpan Raval
Robust Lock-Down Optimization for COVID-19 Policy Guidance. AI4SG@AAAI Fall Symposium (2020)
Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Network Formation under Random Attack and Probabilistic Spread. IJCAI (2019)
Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
Equilibrium Characterization for Data Acquisition Games. IJCAI (2019)
Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
Equilibrium Characterization for Data Acquisition Games. CoRR (2019)
Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Network Formation under Random Attack and Probabilistic Spread. CoRR (2019)
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
Fair Algorithms for Learning in Allocation Problems. FAT (2019)
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
Fair Algorithms for Learning in Allocation Problems. CoRR (2018)
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
A Convex Framework for Fair Regression. CoRR (2017)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Reinforcement Learning. ICML (2017)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fair Learning in Markovian Environments. CoRR (2016)
Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. NIPS (2016)
Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Strategic Network Formation with Attack and Immunization. WINE (2016)
Sepehr Assadi, Justin Hsu, Shahin Jabbari
Online Assignment of Heterogeneous Tasks in Crowdsourcing Markets. HCOMP (2015)
Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Strategic Network Formation with Attack and Immunization. CoRR (2015)
Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. CoRR (2015)
Sepehr Assadi, Justin Hsu, Shahin Jabbari
Online Assignment of Heterogeneous Tasks in Crowdsourcing Markets. CoRR (2015)
Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan
Adaptive Task Assignment for Crowdsourced Classification. ICML (1) (2013)
Shahin Jabbari, Robert C. Holte, Sandra Zilles
PAC-Learning with General Class Noise Models. KI (2012)