| Explanations based on the missing: Towards contrastive explanations with pertinent negatives A Dhurandhar, PY Chen, R Luss, CC Tu, P Ting, K Shanmugam, P Das Advances in Neural Information Proc. Systems, 2018 | 451 | 2018 |
| One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... arXiv preprint arXiv:1909.03012, 2019 | 276 | 2019 |
| Predicting human olfactory perception from chemical features of odor molecules A Keller, RC Gerkin, Y Guan, A Dhurandhar, G Turu, B Szalai, ... Science 355 (6327), 820-826, 2017 | 223 | 2017 |
| Invariant risk minimization games K Ahuja, K Shanmugam, K Varshney, A Dhurandhar International Conference on Machine Learning, 145-155, 2020 | 167 | 2020 |
| Efficient data representation by selecting prototypes with importance weights KS Gurumoorthy, A Dhurandhar, G Cecchi, C Aggarwal 2019 IEEE International Conference on Data Mining (ICDM), 260-269, 2019 | 119* | 2019 |
| TED: Teaching AI to explain its decisions M Hind, D Wei, M Campbell, NCF Codella, A Dhurandhar, A Mojsilović, ... Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 123-129, 2019 | 102 | 2019 |
| Ai explainability 360: An extensible toolkit for understanding data and machine learning models V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... Journal of Machine Learning Research 21 (130), 1-6, 2020 | 58* | 2020 |
| Leveraging latent features for local explanations R Luss, PY Chen, A Dhurandhar, P Sattigeri, Y Zhang, K Shanmugam, ... Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 56* | 2021 |
| Model agnostic contrastive explanations for structured data A Dhurandhar, T Pedapati, A Balakrishnan, PY Chen, K Shanmugam, ... arXiv preprint arXiv:1906.00117, 2019 | 55 | 2019 |
| System and method for identifying procurement fraud/risk A Dhurandhar, MR Ettl, BC Graves, RK Ravi US Patent App. 14/186,071, 2015 | 55 | 2015 |
| Empirical or invariant risk minimization? a sample complexity perspective K Ahuja, J Wang, A Dhurandhar, K Shanmugam, KR Varshney Intl Conference on Learning Representations, 2021 | 53 | 2021 |
| Improving simple models with confidence profiles A Dhurandhar, K Shanmugam, R Luss, P Olsen Advances in Neural Information Proc. Systems, 2018 | 48 | 2018 |
| One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.(2019) V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... arXiv preprint arXiv:1909.03012, 1909 | 47 | 1909 |
| Probabilistic characterization of nearest neighbor classifier A Dhurandhar, A Dobra International journal of machine learning and cybernetics 4, 259-272, 2013 | 41 | 2013 |
| Predicting natural language descriptions of mono-molecular odorants ED Gutiérrez, A Dhurandhar, A Keller, P Meyer, GA Cecchi Nature communications 9 (1), 4979, 2018 | 39 | 2018 |
| Tip: Typifying the interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1706.02952, 2017 | 36 | 2017 |
| Model agnostic multilevel explanations KN Ramamurthy, B Vinzamuri, Y Zhang, A Dhurandhar Advances in Neural Information Proc. Systems, 2020 | 35* | 2020 |
| Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making C Rastogi, Y Zhang, D Wei, KR Varshney, A Dhurandhar, R Tomsett ACM Conference On Computer- Supported Cooperative Work And Social Computing, 2022 | 27 | 2022 |
| Learning global transparent models consistent with local contrastive explanations T Pedapati, A Balakrishnan, K Shanmugam, A Dhurandhar Advances in Neural Information Proc. Systems, 2020 | 26 | 2020 |
| A formal framework to characterize interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1707.03886, 2017 | 26 | 2017 |
