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Conference and Workshop Papers
- 2024
[c10]David O'Brien
, Sumon Biswas, Sayem Mohammad Imtiaz, Rabe Abdalkareem, Emad Shihab, Hridesh Rajan:
Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot. ICSE 2024: 219:1-219:13- 2023
- [c9]Usman Gohar, Sumon Biswas, Hridesh Rajan:
Towards Understanding Fairness and its Composition in Ensemble Machine Learning. ICSE 2023: 1533-1545 - [c8]Sumon Biswas, Hridesh Rajan:
Fairify: Fairness Verification of Neural Networks. ICSE 2023: 1546-1558 - [c7]Sumon Biswas, Yining She, Eunsuk Kang:
Towards Safe ML-Based Systems in Presence of Feedback Loops. SE4SafeML@SIGSOFT FSE 2023: 18-21 - [c6]Giang Nguyen, Sumon Biswas, Hridesh Rajan:
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML. ESEC/SIGSOFT FSE 2023: 502-514 - 2022
- [c5]Sumon Biswas, Mohammad Wardat, Hridesh Rajan:
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. ICSE 2022: 2091-2103 - [c4]David O'Brien, Sumon Biswas, Sayem Imtiaz, Rabe Abdalkareem, Emad Shihab, Hridesh Rajan:
23 shades of self-admitted technical debt: an empirical study on machine learning software. ESEC/SIGSOFT FSE 2022: 734-746 - 2021
- [c3]Sumon Biswas, Hridesh Rajan:
Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline. ESEC/SIGSOFT FSE 2021: 981-993 - 2020
- [c2]Sumon Biswas, Hridesh Rajan:
Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. ESEC/SIGSOFT FSE 2020: 642-653 - 2019
- [c1]Sumon Biswas, Md Johirul Islam, Yijia Huang, Hridesh Rajan:
Boa meets python: a boa dataset of data science software in python language. MSR 2019: 577-581
Informal and Other Publications
- 2023
- [i7]Giang Nguyen, Sumon Biswas, Hridesh Rajan:
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML. CoRR abs/2306.09297 (2023) - 2022
- [i6]Rangeet Pan, Sumon Biswas, Mohna Chakraborty, Breno Dantas Cruz, Hridesh Rajan:
An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models. CoRR abs/2212.00105 (2022) - [i5]Usman Gohar, Sumon Biswas, Hridesh Rajan:
Towards Understanding Fairness and its Composition in Ensemble Machine Learning. CoRR abs/2212.04593 (2022) - [i4]Sumon Biswas, Hridesh Rajan:
Fairify: Fairness Verification of Neural Networks. CoRR abs/2212.06140 (2022) - 2021
- [i3]Sumon Biswas, Hridesh Rajan:
Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline. CoRR abs/2106.06054 (2021) - [i2]Sumon Biswas, Mohammad Wardat, Hridesh Rajan:
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. CoRR abs/2112.01590 (2021) - 2020
- [i1]Sumon Biswas, Hridesh Rajan:
Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness. CoRR abs/2005.12379 (2020)
Coauthor Index
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