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Sahil Verma
Sahil Verma
IIT Kanpur → University of Washington, Seattle
Verified email at cs.washington.edu - Homepage
Title
Cited by
Cited by
Year
Fairness definitions explained
S Verma, J Rubin
2018 IEEE/ACM International Workshop on Software Fairness (FairWare), 1-7, 2018
8442018
Counterfactual explanations for machine learning: A review
S Verma, J Dickerson, K Hines
arXiv preprint arXiv:2010.10596, 2020
3482020
Synergistic debug-repair of heap manipulations
S Verma, S Roy
Proceedings of the 2017 11th Joint Meeting on Foundations of Software …, 2017
202017
Removing biased data to improve fairness and accuracy
S Verma, M Ernst, R Just
arXiv preprint arXiv:2102.03054, 2021
162021
Counterfactual Explanations for Machine Learning: Challenges Revisited
S Verma, J Dickerson, K Hines
arXiv preprint arXiv:2106.07756, 2021
152021
Facets of fairness in search and recommendation
S Verma, R Gao, C Shah
Bias and Social Aspects in Search and Recommendation: First International …, 2020
152020
Shapeflow: Dynamic shape interpreter for tensorflow
S Verma, Z Su
arXiv preprint arXiv:2011.13452, 2020
122020
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models
S Verma, K Hines, JP Dickerson
AAAI 2022, 2022
11*2022
Pitfalls of explainable ML: an industry perspective
S Verma, A Lahiri, JP Dickerson, SI Lee
arXiv preprint arXiv:2106.07758, 2021
62021
Debug-localize-repair: a symbiotic construction for heap manipulations
S Verma, S Roy
Formal Methods in System Design 58 (3), 399-439, 2021
52021
Benchmarking Symbolic Execution Using Constraint Problems-Initial Results
S Verma, RHC Yap
2019 IEEE 31st International Conference on Tools with Artificial …, 2019
32019
RecXplainer: Post-Hoc Attribute-Based Explanations for Recommender Systems
S Verma, A Beniwal, N Sadagopan, A Seshadri
arXiv preprint arXiv:2211.14935, 2022
12022
Methods and apparatus for generating fast counterfactual explanations for black-box models using reinforcement learning (Patent)
S Verma, J Dickerson, K Hines
US Patent 11,403,538, 2022
2022
NAP: Noise-Based Sensitivity Analysis for Programs
J Michel*, S Verma*, B Sherman, M Carbin
WAX 2019, 2019
2019
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Articles 1–14