1 citations · 2 across the 7 of their papers we have counts for
7 papers
Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models
Ishaan Gangwani, Aayam Bansal
Zero-shot foundation models (FMs) promise training-free prediction on tabular data, yet their hardware footprint remains poorly characterized. We present a fully reproducible bench…
Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams
Aayam Bansal, Ishaan Gangwani
We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Throug…
MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data
Aayam Bansal, Ishaan Gangwani
Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We in…
Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
Aayam Bansal
As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeo…
Emotional Analysis of Fashion Trends Using Social Media and AI: Sentiment Analysis on Twitter for Fashion Trend Forecasting
Aayam Bansal, Agneya Tharun
This study explores the intersection of fashion trends and social media sentiment through computational analysis of Twitter data using the T4SA (Twitter for Sentiment Analysis) dat…
Linguistic Complexity and Socio-cultural Patterns in Hip-Hop Lyrics
Aayam Bansal, Raghav Agarwal, Kaashvi Jain
This paper presents a comprehensive computational framework for analyzing linguistic complexity and socio-cultural trends in hip-hop lyrics. Using a dataset of 3,814 songs from 146…