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 | 717 | 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 | 471 | 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 | 306 | 2017 |
Invariant risk minimization games K Ahuja, K Shanmugam, K Varshney, A Dhurandhar International Conference on Machine Learning, 145-155, 2020 | 271 | 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 | 193* | 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 | 160 | 2019 |
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 | 117 | 2022 |
System and method for identifying procurement fraud/risk A Dhurandhar, MR Ettl, BC Graves, RK Ravi US Patent App. 14/186,071, 2015 | 105 | 2015 |
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 | 102* | 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 | 86* | 2021 |
Model agnostic contrastive explanations for structured data A Dhurandhar, T Pedapati, A Balakrishnan, PY Chen, K Shanmugam, ... arXiv preprint arXiv:1906.00117, 2019 | 83 | 2019 |
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 | 80 | 2021 |
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 | 77 | 1909 |
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI QV Liao, Y Zhang, R Luss, F Doshi-Velez, A Dhurandhar Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022 | 69 | 2022 |
Improving simple models with confidence profiles A Dhurandhar, K Shanmugam, R Luss, P Olsen Advances in Neural Information Proc. Systems, 2018 | 68 | 2018 |
Model agnostic multilevel explanations KN Ramamurthy, B Vinzamuri, Y Zhang, A Dhurandhar Advances in Neural Information Proc. Systems, 2020 | 57* | 2020 |
Predicting natural language descriptions of mono-molecular odorants ED Gutiérrez, A Dhurandhar, A Keller, P Meyer, GA Cecchi Nature communications 9 (1), 4979, 2018 | 56 | 2018 |
Tip: Typifying the interpretability of procedures A Dhurandhar, V Iyengar, R Luss, K Shanmugam arXiv preprint arXiv:1706.02952, 2017 | 44 | 2017 |
Ranking and tracking suspicious procurement entities A Dhurandhar, M Ettl, BC Graves, G Maniachari, AT Mazzatti, RK Ravi US Patent 10,467,631, 2019 | 42 | 2019 |
Probabilistic characterization of nearest neighbor classifier A Dhurandhar, A Dobra International journal of machine learning and cybernetics 4, 259-272, 2013 | 42 | 2013 |