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ORCIDVaishnavi Bhargava
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2020 – today
- 2026
[i9]Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava, Valerie Chen, Debojyoti Dutta:
SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories. CoRR abs/2601.13713 (2026)- 2025
- [i8]Datta Nimmaturi, Vaishnavi Bhargava, Rajat Ghosh, Johnu George, Debojyoti Dutta:
Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models. CoRR abs/2507.18014 (2025) - [i7]Jinan Zhou, Rajat Ghosh, Vaishnavi Bhargava, Debojyoti Dutta, Aryan Singhal:
BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning. CoRR abs/2507.23170 (2025) - [i6]Yashshi Pipalani, Hritik Raj, Rajat Ghosh, Vaishnavi Bhargava, Debojyoti Dutta:
Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go. CoRR abs/2511.10868 (2025) - 2024
- [c4]Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala:
The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators. NeurIPS 2024 - [i5]Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala:
The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators. CoRR abs/2407.11004 (2024) - [i4]Vaishnavi Bhargava, Rajat Ghosh, Debojyoti Dutta:
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++? CoRR abs/2412.02735 (2024) - 2022
- [c3]Romaric Gaudel, Luis Galárraga, Julien Delaunay
, Laurence Rozé, Vaishnavi Bhargava:
s-LIME: Reconciling Locality and Fidelity in Linear Explanations. IDA 2022: 102-114 - [i3]Romaric Gaudel, Luis Galárraga, Julien Delaunay, Laurence Rozé, Vaishnavi Bhargava:
s-LIME: Reconciling Locality and Fidelity in Linear Explanations. CoRR abs/2208.01510 (2022) - 2020
- [c2]Guilherme Alves, Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli:
Making ML Models Fairer Through Explanations: The Case of LimeOut. AIST 2020: 3-18 - [c1]Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli:
LimeOut: An Ensemble Approach to Improve Process Fairness. PKDD/ECML Workshops 2020: 475-491 - [i2]Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli:
LimeOut: An Ensemble Approach To Improve Process Fairness. CoRR abs/2006.10531 (2020) - [i1]Guilherme Alves, Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli:
Making ML models fairer through explanations: the case of LimeOut. CoRR abs/2011.00603 (2020)
Coauthor Index
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