| 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 | 1683 | 2018 |
| 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 | 1047* | 2020 |
| 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 | 1014 | 2016 |
| 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 | 690* | 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 | 331 | 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 | 321 | 2020 |
| The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective S Krishna, T Han, A Gu, S Jabbari, S Wu, H Lakkaraju | 237 | 2023 |
| 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 | 236 | 2015 |
| 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 | 227 | 2021 |
| 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 | 223 | 2021 |
| 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 | 213 | 2017 |
| 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 | 209 | 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 | 204 | 2013 |
| 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 | 166 | 2022 |
| Counterfactual Explanations Can Be Manipulated D Slack, S Hilgard, H Lakkaraju, S Singh Advances in Neural Information Processing Systems (NeurIPS), 2021 | 166 | 2021 |
| 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 | 143 | 2011 |
| Certifying llm safety against adversarial prompting A Kumar, C Agarwal, S Srinivas, AJ Li, S Feizi, H Lakkaraju arXiv preprint arXiv:2309.02705, 2023 | 139 | 2023 |
| Learning cost-effective and interpretable treatment regimes H Lakkaraju, C Rudin AISTATS, 2017, 2017 | 138* | 2017 |
| Evaluating explainability for graph neural networks C Agarwal, O Queen, H Lakkaraju, M Zitnik Nature Scientific Data 10 (1), 144, 2023 | 135 | 2023 |
| Aspect Specific Sentiment Analysis using Hierarchical Deep Learning H Lakkaraju, R Socher, C Manning NIPS Workshop on Deep Learning and Representation Learning, 2014 | 125 | 2014 |
Chirag AgarwalAssistant Professor, UVAVerified email at virginia.edu
