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Joymallya Chakraborty
Joymallya Chakraborty
Applied Scientist @Amazon.com
Verified email at ncsu.edu - Homepage
Title
Cited by
Cited by
Year
Bias in Machine Learning Software: Why? How? What to do?
J Chakraborty, S Majumder, T Menzies
Proceedings of the 2021 29th ACM Joint Meeting on European Software …, 2021
1572021
Fairway: A Way to Build Fair ML Software
J Chakraborty, S Majumder, Z Yu, T Menzies
Proceedings of the 2020 28th ACM Joint Meeting on European Software …, 2020
1122020
Investigating the effects of gender bias on GitHub
N Imtiaz, J Middleton, J Chakraborty, N Robson, G Bai, E Murphy-Hill
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019
103*2019
Making Fair ML Software using Trustworthy Explanation
J Chakraborty, S Majumder, Z Yu, T Menzies
The 35th IEEE/ACM International Conference on Automated Software Engineering, 2020
452020
Software engineering for fairness: A case study with hyperparameter optimization
J Chakraborty, T Xia, FM Fahid, T Menzies
arXiv preprint arXiv:1905.05786, 2019
412019
Algorithms for generating all possible spanning trees of a simple undirected connected graph: an extensive review
M Chakraborty, S Chowdhury, J Chakraborty, R Mehera, RK Pal
Complex & Intelligent Systems 5, 265-281, 2019
352019
FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes
K Peng, J Chakraborty, T Menzies
IEEE Transactions on Software Engineering 49 (4), 2426–2439, 2022
22*2022
Fair enough: Searching for sufficient measures of fairness
S Majumder, J Chakraborty, GR Bai, KT Stolee, T Menzies
ACM Transactions on Software Engineering and Methodology, 2023
182023
Fair-SSL: Building fair ML Software with less data
J Chakraborty, H Tu, S Majumder
International Workshop on Equitable Data and Technology (FairWare ’22 ), 2022
15*2022
Why software projects need heroes (lessons learned from 1100+ projects)
S Majumder, J Chakraborty, A Agrawal, T Menzies
arXiv preprint arXiv:1904.09954, 2019
132019
Fair balance: Mitigating machine learning bias against multiple protected attributes with data balancing
Z Yu
arXiv preprint arXiv:2107.08310 17, 2021
102021
Predicting breakdowns in cloud services (with SPIKE)
J Chen, J Chakraborty, P Clark, K Haverlock, S Cherian, T Menzies
Proceedings of the 2019 27th ACM joint meeting on european software …, 2019
82019
FairBalance: Improving machine learning fairness on multiple sensitive attributes with data balancing
Z Yu, J Chakraborty, T Menzies
arXiv preprint Arxiv:2107.08310, 2021
72021
Fairer machine learning software on multiple sensitive attributes with data preprocessing
Z Yu, J Chakraborty, T Menzies
arXiv preprint arXiv:2107.08310, 2021
32021
When less is more: on the value of “co-training” for semi-supervised software defect predictors
S Majumder, J Chakraborty, T Menzies
Empirical Software Engineering 29 (2), 1-33, 2024
12024
Communication and Code Dependency Effects on Software Code Quality: An Empirical Analysis of Herbsleb Hypothesis
S Majumder, J Chakraborty, A Agrawal, T Menzies
arXiv preprint arXiv:1904.09954, 2019
12019
Deciphering Ml Software Fairness
J Chakraborty
North Carolina State University, 2022
2022
FairBalance: How to Achieve Equalized Odds With Data Pre-processing
Z Yu, J Chakraborty, T Menzies
arXiv preprint arXiv:2107.08310, 2021
2021
FairWare 2022
S Alimadadi, C Bird, M Canellas, J Chakraborty, D Ford, L Hampton, ...
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