Himabindu Lakkaraju

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Jure LeskovecProfessor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Chirag AgarwalAssistant Professor, UVADirección de correo verificada de virginia.edu
Dylan SlackGoogleDirección de correo verificada de google.com
Sameer SinghAssociate Professor, UC IrvineDirección de correo verificada de uci.edu
Ece KamarMicrosoft ResearchDirección de correo verificada de microsoft.com
Martin PawelczykPostdoc, Harvard UniversityDirección de correo verificada de uni-tuebingen.de
Marinka ZitnikAssistant Professor, Harvard UniversityDirección de correo verificada de hms.harvard.edu
Jon KleinbergProfessor of Computer Science, Cornell UniversityDirección de correo verificada de cs.cornell.edu
Sendhil MullainathanUniversity of ChicagoDirección de correo verificada de chicagobooth.edu
Rich CaruanaMicrosoft ResearchDirección de correo verificada de microsoft.com
Jens LudwigUniversity of ChicagoDirección de correo verificada de uchicago.edu
Stephen H. BachAssistant Professor of Computer Science, Brown UniversityDirección de correo verificada de cs.brown.edu
Osbert BastaniUniversity of PennsylvaniaDirección de correo verificada de seas.upenn.edu
Shahin JabbariDrexel UniversityDirección de correo verificada de drexel.edu
Cynthia RudinProfessor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke UniversityDirección de correo verificada de cs.duke.edu
Eric HorvitzMicrosoftDirección de correo verificada de microsoft.com
Chiranjib BhattacharyyaProfessor, Indian Institute of ScienceDirección de correo verificada de csa.iisc.ernet.in
Indrajit BhattacharyaTCS ResearchDirección de correo verificada de tcs.com
Rayid GhaniCarrnegie Mellon UniversityDirección de correo verificada de rayidghani.com
Julian McAuleyProfessor, UC San DiegoDirección de correo verificada de eng.ucsd.edu

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Himabindu Lakkaraju

Assistant Professor, Harvard University

Dirección de correo verificada de seas.harvard.edu - Página principal

Explainable & Fair ML Adversarial Robustness Human Centric ML ML for Healthcare & Law


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Human decisions and machine predictions J Kleinberg, H Lakkaraju, J Leskovec, J Ludwig, S Mullainathan The quarterly journal of economics 133 (1), 237-293, 2018	1585	2018
Interpretable Decision Sets: A Joint Framework for Description and Prediction H Lakkaraju, SH Bach, J Leskovec ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016	957	2016
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods D Slack, S Hilgard, E Jia, S Singh, H Lakkaraju AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020	927*	2020
Faithful and customizable explanations of black box models H Lakkaraju, E Kamar, R Caruana, J Leskovec Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 131-138, 2019	649*	2019
Mining big data to extract patterns and predict real-life outcomes. M Kosinski, Y Wang, H Lakkaraju, J Leskovec Psychological methods 21 (4), 493, 2016	323	2016
" How do I fool you?" Manipulating User Trust via Misleading Black Box Explanations H Lakkaraju, O Bastani Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 79-85, 2020	288	2020
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes H Lakkaraju, E Aguiar, C Shan, D Miller, N Bhanpuri, R Ghani, ... ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015	224	2015
Identifying unknown unknowns in the open world: Representations and policies for guided exploration H Lakkaraju, E Kamar, R Caruana, E Horvitz Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017	206	2017
Reliable post hoc explanations: Modeling uncertainty in explainability D Slack, A Hilgard, S Singh, H Lakkaraju Advances in neural information processing systems 34, 9391-9404, 2021	201	2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning C Agarwal, H Lakkaraju, M Zitnik Conference on Uncertainty in Artifical Intelligence (UAI), 2021	201	2021
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective S Krishna, T Han, A Gu, S Jabbari, S Wu, H Lakkaraju	199	2023
What's in a name? understanding the interplay between titles, content, and communities in social media H Lakkaraju, J McAuley, J Leskovec Proceedings of the international AAAI conference on web and social media 7 …, 2013	195	2013
The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables H Lakkaraju, J Kleinberg, J Leskovec, J Ludwig, S Mullainathan Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017	189	2017
Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments H Lakkaraju, C Bhattacharya, I Bhattacharya, S Merugu SIAM International Conference on Data Mining, 2011	141	2011
Counterfactual Explanations Can Be Manipulated D Slack, S Hilgard, H Lakkaraju, S Singh Advances in Neural Information Processing Systems (NeurIPS), 2021	140	2021
Openxai: Towards a transparent evaluation of model explanations C Agarwal, S Krishna, E Saxena, M Pawelczyk, N Johnson, I Puri, M Zitnik, ... Advances in neural information processing systems 35, 15784-15799, 2022	134	2022
Learning cost-effective and interpretable treatment regimes H Lakkaraju, C Rudin AISTATS, 2017, 2017	129*	2017
Aspect Specific Sentiment Analysis using Hierarchical Deep Learning H Lakkaraju, R Socher, C Manning NIPS Workshop on Deep Learning and Representation Learning, 2014	123	2014
Towards Robust and Reliable Algorithmic Recourse S Upadhyay, S Joshi, H Lakkaraju Advances in Neural Information Processing Systems (NeurIPS), 2021	103	2021
Evaluating explainability for graph neural networks C Agarwal, O Queen, H Lakkaraju, M Zitnik Nature Scientific Data 10 (1), 144, 2023	92	2023

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