Karuna Bhaila Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
Biography and Research Information
OverviewAI-generated summary
Karuna Bhaila's research focuses on advancing artificial intelligence, particularly in areas related to privacy, fairness, and causal reasoning. Recent publications investigate differentially private methods for in-context learning with tabular data, introducing the DP-TabICL framework. Bhaila has also explored the fairness implications of randomized response techniques in machine learning models, finding no disparate impact on accuracy. Further work examines fair in-context learning through latent concept variables. In the realm of vision-language models, research includes unlearning techniques guided by cross-modal attention and benchmarking visual causal reasoning capabilities. Bhaila's work contributes to the development of more robust, fair, and interpretable AI systems.
Metrics
- h-index: 3
- Publications: 8
- Citations: 19
Selected Publications
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Vulnerability Analysis of Integrated Power and Gas System Based on Influence Graph (2025)
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Fair In-Context Learning via Latent Concept Variables (2025)
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CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models (2025)
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Soft Prompting for Unlearning in Large Language Models (2025)
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DP-TabICL: In-Context Learning with Differentially Private Tabular Data (2024)
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Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks (2024)
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Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks (2024)
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Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach (2024)
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Randomized Response Has No Disparate Impact on Model Accuracy (2023)
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Fair Collective Classification in Networked Data (2022)
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