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Joymallya Chakraborty
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2020 – today
- 2024
- [j4]Suvodeep Majumder
, Joymallya Chakraborty, Tim Menzies
When less is more: on the value of "co-training" for semi-supervised software defect predictors. Empir. Softw. Eng. 29(2): 51 (2024) - [j3]Zhe Yu
FairBalance: How to Achieve Equalized Odds With Data Pre-Processing. IEEE Trans. Software Eng. 50(9): 2294-2312 (2024) - [i11]Joymallya Chakraborty, Wei Xia, Anirban Majumder, Dan Ma, Walid Chaabene, Naveed Janvekar:
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection. CoRR abs/2409.06072 (2024) - 2023
- [j2]Suvodeep Majumder
Fair Enough: Searching for Sufficient Measures of Fairness. ACM Trans. Softw. Eng. Methodol. 32(6): 134:1-134:22 (2023) - [j1]Kewen Peng
FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes. IEEE Trans. Software Eng. 49(4): 2426-2439 (2023) - 2022
- [c6]Joymallya Chakraborty, Suvodeep Majumder, Huy Tu:
Fair-SSL: Building fair ML Software with less data. FairWare@ICSE 2022: 1-8 - [i10]Suvodeep Majumder, Joymallya Chakraborty, Tim Menzies:
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors. CoRR abs/2211.05920 (2022) - 2021
- [c5]Joymallya Chakraborty, Suvodeep Majumder, Tim Menzies
Bias in machine learning software: why? how? what to do? ESEC/SIGSOFT FSE 2021: 429-440 - [i9]Joymallya Chakraborty, Suvodeep Majumder, Tim Menzies:
Bias in Machine Learning Software: Why? How? What to do? CoRR abs/2105.12195 (2021) - [i8]Kewen Peng, Joymallya Chakraborty, Tim Menzies:
xFAIR: Better Fairness via Model-based Rebalancing of Protected Attributes. CoRR abs/2110.01109 (2021) - [i7]Suvodeep Majumder, Joymallya Chakraborty, Gina R. Bai, Kathryn T. Stolee, Tim Menzies:
Fair Enough: Searching for Sufficient Measures of Fairness. CoRR abs/2110.13029 (2021) - [i6]Joymallya Chakraborty, Huy Tu, Suvodeep Majumder, Tim Menzies:
Can We Achieve Fairness Using Semi-Supervised Learning? CoRR abs/2111.02038 (2021) - 2020
- [c4]Joymallya Chakraborty, Kewen Peng, Tim Menzies
Making Fair ML Software using Trustworthy Explanation. ASE 2020: 1229-1233 - [c3]Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu, Tim Menzies:
Fairway: a way to build fair ML software. ESEC/SIGSOFT FSE 2020: 654-665 - [i5]Joymallya Chakraborty, Suvodeep Majumder, Zhe Wu, Tim Menzies:
Fairway: SE Principles for Building Fairer Software. CoRR abs/2003.10354 (2020) - [i4]Joymallya Chakraborty, Kewen Peng, Tim Menzies:
Making Fair ML Software using Trustworthy Explanation. CoRR abs/2007.02893 (2020)
2010 – 2019
- 2019
- [c2]Nasif Imtiaz, Justin Middleton, Joymallya Chakraborty, Neill Robson, Gina R. Bai
Investigating the effects of gender bias on GitHub. ICSE 2019: 700-711 - [c1]Jianfeng Chen, Joymallya Chakraborty, Philip Clark, Kevin Haverlock, Snehit Cherian, Tim Menzies
Predicting breakdowns in cloud services (with SPIKE). ESEC/SIGSOFT FSE 2019: 916-924 - [i3]Suvodeep Majumder, Joymallya Chakraborty, Amritanshu Agrawal, Tim Menzies:
Why Software Projects need Heroes (Lessons Learned from 1100+ Projects). CoRR abs/1904.09954 (2019) - [i2]Joymallya Chakraborty, Tianpei Xia, Fahmid M. Fahid, Tim Menzies:
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization. CoRR abs/1905.05786 (2019) - [i1]Jianfeng Chen, Joymallya Chakraborty, Philip Clark, Kevin Haverlock, Snehit Cherian, Tim Menzies:
Predicting Breakdowns in Cloud Services (with SPIKE). CoRR abs/1905.06390 (2019)
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