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Himabindu Lakkaraju
Himabindu Lakkaraju
Assistant Professor, Harvard University
Bestätigte E-Mail-Adresse bei seas.harvard.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
16372018
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
9902016
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
984*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
667*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
3262016
" 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
3062020
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
2302015
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
S Krishna, T Han, A Gu, S Jabbari, S Wu, H Lakkaraju
2172023
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
2152021
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
2142021
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
2102017
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
2002017
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
2002013
Counterfactual Explanations Can Be Manipulated
D Slack, S Hilgard, H Lakkaraju, S Singh
Advances in Neural Information Processing Systems (NeurIPS), 2021
1582021
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
1502022
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
1412011
Learning cost-effective and interpretable treatment regimes
H Lakkaraju, C Rudin
AISTATS, 2017, 2017
134*2017
Aspect Specific Sentiment Analysis using Hierarchical Deep Learning
H Lakkaraju, R Socher, C Manning
NIPS Workshop on Deep Learning and Representation Learning, 2014
1242014
Evaluating explainability for graph neural networks
C Agarwal, O Queen, H Lakkaraju, M Zitnik
Nature Scientific Data 10 (1), 144, 2023
1152023
Towards Robust and Reliable Algorithmic Recourse
S Upadhyay, S Joshi, H Lakkaraju
Advances in Neural Information Processing Systems (NeurIPS), 2021
1152021
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