Umang Bhatt

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20182019202020214 12 98 28

Co-authors

Adrian WellerPrincipal Research Fellow, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Jose M. F. MouraCarnegie Mellon UniversityVerified email at ece.cmu.edu
Alice XiangSenior Research Scientist (AI Ethics Lead), Sony AIVerified email at sony.com
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. of Texas at Austin; Faculty Dell Med, UTCS, McCombs, UT-BMEVerified email at utexas.edu
Ankur TalyStaff Research Scientist, GoogleVerified email at google.com
Pradeep RavikumarAssociate Professor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
Fei FangCarnegie Mellon UniversityVerified email at cmu.edu
Manuela M. VelosoHerbert A. Simon University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Radu MarculescuThe University of Texas at AustinVerified email at utexas.edu
José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
Isabel ValeraSaarland University and Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de

Umang Bhatt

University of Cambridge

Verified email at cam.ac.uk - Homepage

Machine Learning Artificial Intelligence Explainability


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Explainable machine learning in deployment U Bhatt, A Xiang, S Sharma, A Weller, A Taly, Y Jia, J Ghosh, R Puri, ... ACM Conference on Fairness, Accountability, and Transparency (FAT* '20), 2020	66	2020
Intelligent pothole detection and road condition assessment U Bhatt, S Mani, E Xi, JZ Kolter arXiv preprint arXiv:1710.02595, 2017	21	2017
You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods. B Dimanov, U Bhatt, M Jamnik, A Weller ECAI 2020, 2020	15	2020
Evaluating and Aggregating Feature-based Model Explanations U Bhatt, A Weller, JMF Moura IJCAI 2020, 2020	11	2020
Machine learning explainability for external stakeholders U Bhatt, MK Andrus, A Weller, A Xiang arXiv preprint arXiv:2007.05408, 2020	5	2020
Building Human-Machine Trust via Interpretability U Bhatt, P Ravikumar, J Moura Proceedings of the AAAI Conference on Artificial Intelligence 33, 9919-9920, 2019	5	2019
Towards aggregating weighted feature attributions U Bhatt, P Ravikumar, JMF Moura arXiv preprint arXiv:1901.10040, 2019	5	2019
On the fairness of causal algorithmic recourse J von Kügelgen, U Bhatt, AH Karimi, I Valera, A Weller, B Schölkopf NeurIPS 2020 Workshop Algorithmic Fairness through the Lens of Causality and …, 2020	3	2020
On Network Science and Mutual Information for Explaining Deep Neural Networks B Davis, U Bhatt, K Bhardwaj, R Marculescu, JMF Moura ICASSP 2020, 2020	3	2020
Getting a clue: A method for explaining uncertainty estimates J Antorán, U Bhatt, T Adel, A Weller, JM Hernández-Lobato ICLR 2021, 2020	2	2020
A Robot’s Expressive Language Affects Human Strategy and Perceptions in a Competitive Game AM Roth, S Reig, U Bhatt, J Shulgach, T Amin, A Doryab, F Fang, ... IEEE RO-MAN 2019, 2019	2	2019
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting AM Roth, U Bhatt, T Amin, A Doryab, F Fang, M Veloso arXiv preprint arXiv:1806.03671, 2018	2	2018
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty U Bhatt, Y Zhang, J Antorán, QV Liao, P Sattigeri, R Fogliato, ... arXiv preprint arXiv:2011.07586, 2020	1	2020
NIF: A Framework for Quantifying Neural Information Flow in Deep Networks B Davis, U Bhatt, K Bhardwaj, R Marculescu, J Moura AAAI 2019 Workshop on Network Interpretability for Deep Learning, 2019	1	2019
Maintaining The Humanity of Our Models U Bhatt AAAI 2018 Spring Symposium: AI and Society, 2017	1	2017
Diagnostic Model Explanations: A Medical Narrative U Bhatt, B Davis, JMF Moura AAAI 2019 Spring Symposium: Interpretable AI for Well-being, 2019		2019

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