Amit Dhurandhar
Amit Dhurandhar
Principal RSM, IBM
Verified email at - Homepage
Cited by
Cited by
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
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
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
Invariant risk minimization games
K Ahuja, K Shanmugam, K Varshney, A Dhurandhar
International Conference on Machine Learning, 145-155, 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
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
System and method for identifying procurement fraud/risk
A Dhurandhar, MR Ettl, BC Graves, RK Ravi
US Patent App. 14/186,071, 2015
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
Model agnostic contrastive explanations for structured data
A Dhurandhar, T Pedapati, A Balakrishnan, PY Chen, K Shanmugam, ...
arXiv preprint arXiv:1906.00117, 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
Improving simple models with confidence profiles
A Dhurandhar, K Shanmugam, R Luss, P Olsen
Advances in Neural Information Proc. Systems, 2018
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
Probabilistic characterization of nearest neighbor classifier
A Dhurandhar, A Dobra
International journal of machine learning and cybernetics 4 (4), 259-272, 2013
Predicting natural language descriptions of mono-molecular odorants
ED Gutiérrez, A Dhurandhar, A Keller, P Meyer, GA Cecchi
Nature communications 9 (1), 1-12, 2018
Tip: Typifying the interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1706.02952, 2017
Model agnostic multilevel explanations
KN Ramamurthy, B Vinzamuri, Y Zhang, A Dhurandhar
Advances in Neural Information Proc. Systems, 2020
A formal framework to characterize interpretability of procedures
A Dhurandhar, V Iyengar, R Luss, K Shanmugam
arXiv preprint arXiv:1707.03886, 2017
Big data system for analyzing risky procurement entities
A Dhurandhar, B Graves, R Ravi, G Maniachari, M Ettl
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
Probabilistic Characterization of Random Decision Trees.
A Dhurandhar, A Dobra
Journal of Machine Learning Research 9 (10), 2008
Detecting electricity theft via meter tampering using statistical methods
A Dhurandhar, JR Kalagnanam, SA Siegel, Y Zhu
US Patent 9,600,773, 2017
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