Making ML Models Fairer Through Explanations: The Case of LimeOut. - Guilherme Alves 001; Vaishnavi Bhargava; Miguel Couceiro; Amedeo Napoli (AIST, 2020)

LimeOut: An Ensemble Approach to Improve Process Fairness. - Vaishnavi Bhargava; Miguel Couceiro; Amedeo Napoli (PKDD/ECML Workshops, 2020)

s-LIME: Reconciling Locality and Fidelity in Linear Explanations. - Romaric Gaudel; Luis Galárraga; Julien Delaunay; Laurence Rozé; Vaishnavi Bhargava (IDA, 2022)

The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators. - Tzu-Heng Huang; Catherine Cao; Vaishnavi Bhargava; Frederic Sala (NeurIPS, 2024)

CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++? - Vaishnavi Bhargava; Rajat Ghosh; Debojyoti Dutta (CoRR, 2024)

Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models. - Datta Nimmaturi; Vaishnavi Bhargava; Rajat Ghosh; Johnu George; Debojyoti Dutta (CoRR, 2025)

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning. - Jinan Zhou; Rajat Ghosh; Vaishnavi Bhargava; Debojyoti Dutta; Aryan Singhal (CoRR, 2025)