Himabindu Lakkaraju

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Jure LeskovecProfessor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Chirag AgarwalPostdoctoral Research Fellow, HarvardDirección de correo verificada de hbs.edu
Dylan SlackUC IrvineDirección de correo verificada de uci.edu
Sameer SinghAssociate Professor, UC IrvineDirección de correo verificada de uci.edu
Ece KamarMicrosoft ResearchDirección de correo verificada de microsoft.com
Jon KleinbergProfessor of Computer Science, Cornell UniversityDirección de correo verificada de cs.cornell.edu
Sendhil MullainathanUniversity of ChicagoDirección de correo verificada de chicagobooth.edu
Marinka ZitnikAssistant Professor, Harvard UniversityDirección de correo verificada de hms.harvard.edu
Rich CaruanaMicrosoft ResearchDirección de correo verificada de microsoft.com
Martin PawelczykPostdoc, Harvard UniversityDirección de correo verificada de uni-tuebingen.de
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
Chiranjib BhattacharyyaProfessor, Indian Institute of ScienceDirección de correo verificada de csa.iisc.ernet.in
Eric HorvitzMicrosoftDirección de correo verificada de microsoft.com
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	1448	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	884	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	764*	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	600*	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	305	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	248	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	208	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	198	2017
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	186	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	171	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	167*	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	163	2021
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective S Krishna, T Han, A Gu, S Jabbari, S Wu, H Lakkaraju	139	2023
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	136	2011
Aspect Specific Sentiment Analysis using Hierarchical Deep Learning H Lakkaraju, R Socher, C Manning NIPS Workshop on Deep Learning and Representation Learning, 2014	119	2014
Counterfactual Explanations Can Be Manipulated D Slack, S Hilgard, H Lakkaraju, S Singh Advances in Neural Information Processing Systems (NeurIPS), 2021	117	2021
Learning cost-effective and interpretable treatment regimes H Lakkaraju, C Rudin AISTATS, 2017, 2017	115*	2017
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	92	2022
Towards Robust and Reliable Algorithmic Recourse S Upadhyay, S Joshi, H Lakkaraju Advances in Neural Information Processing Systems (NeurIPS), 2021	88	2021
Who, when, and why: a machine learning approach to prioritizing students at risk of not graduating high school on time E Aguiar, H Lakkaraju, N Bhanpuri, D Miller, B Yuhas, KL Addison Proceedings of the Fifth International Conference on Learning Analytics And …, 2015	84	2015

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