| Explaining machine learning classifiers through diverse counterfactual explanations RK Mothilal, A Sharma, C Tan Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 1221 | 2020 |
| Prediction and explanation in social systems JM Hofman, A Sharma, DJ Watts Science 355 (6324), 486-488, 2017 | 500 | 2017 |
| Domain generalization using causal matching D Mahajan, S Tople, A Sharma International Conference on Machine Learning, 7313-7324, 2021 | 318 | 2021 |
| Preserving causal constraints in counterfactual explanations for machine learning classifiers D Mahajan, C Tan, A Sharma arXiv preprint arXiv:1912.03277, 2019 | 252 | 2019 |
| Do social explanations work? Studying and modeling the effects of social explanations in recommender systems A Sharma, D Cosley Proceedings of the 22nd international conference on World Wide Web, 1133-1144, 2013 | 204 | 2013 |
| Causal reasoning and large language models: Opening a new frontier for causality E Kıcıman, R Ness, A Sharma, C Tan arXiv preprint arXiv:2305.00050, 2023 | 202 | 2023 |
| Topology-aware task mapping for reducing communication contention on large parallel machines T Agarwal, A Sharma, A Laxmikant, LV Kalé Proceedings 20th IEEE International Parallel & Distributed Processing …, 2006 | 193 | 2006 |
| DoWhy: An end-to-end library for causal inference A Sharma, E Kiciman arXiv preprint arXiv:2011.04216, 2020 | 191 | 2020 |
| Exploring limits to prediction in complex social systems T Martin, JM Hofman, A Sharma, A Anderson, DJ Watts Proceedings of the 25th international conference on world wide web, 683-694, 2016 | 173 | 2016 |
| Estimating the causal impact of recommendation systems from observational data A Sharma, J Hofman, D Watts Proceedings of the Sixteenth ACM Conference on Economics and Computation …, 2015 | 155 | 2015 |
| Predictability of popularity: Gaps between prediction and understanding B Shulman, A Sharma, D Cosley Proceedings of the international AAAI conference on web and social media 10 …, 2016 | 126 | 2016 |
| Technology readiness levels for machine learning systems A Lavin, CM Gilligan-Lee, A Visnjic, S Ganju, D Newman, S Ganguly, ... Nature Communications 13 (1), 6039, 2022 | 115 | 2022 |
| Towards unifying feature attribution and counterfactual explanations: Different means to the same end R Kommiya Mothilal, D Mahajan, C Tan, A Sharma Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 652-663, 2021 | 115 | 2021 |
| Auditing search engines for differential satisfaction across demographics R Mehrotra, A Anderson, F Diaz, A Sharma, H Wallach, E Yilmaz Proceedings of the 26th international conference on World Wide Web companion …, 2017 | 115 | 2017 |
| Workload management for power efficiency in virtualized data centers G Dasgupta, A Sharma, A Verma, A Neogi, R Kothari Communications of the ACM 54 (7), 131-141, 2011 | 90 | 2011 |
| Deep end-to-end causal inference T Geffner, J Antoran, A Foster, W Gong, C Ma, E Kiciman, A Sharma, ... arXiv preprint arXiv:2202.02195, 2022 | 78 | 2022 |
| Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention JA Naslund, PP Gonsalves, O Gruebner, SR Pendse, SL Smith, A Sharma, ... Current Treatment Options in Psychiatry, 1-15, 2019 | 78 | 2019 |
| Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals S Dash, VN Balasubramanian, A Sharma Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 73 | 2022 |
| Moments of change: Analyzing peer-based cognitive support in online mental health forums Y Pruksachatkun, SR Pendse, A Sharma Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019 | 68 | 2019 |
| Study of karyotypes and their alterations in aroids AK Sharma, NK Das Agronomia lusitana 16, 23-48, 1954 | 68 | 1954 |
Chenhao TanUniversity of ChicagoVerified email at chenhaot.com