3 papers
cs.LG2025
Creating a Causally Grounded Rating Method for Assessing the Robustness of AI Models for Time-Series Forecasting
Kausik Lakkaraju, Rachneet Kaur, Parisa Zehtabi +5
AI models, including both time-series-specific and general-purpose Foundation Models (FMs), have demonstrated strong potential in time-series forecasting across sectors like financ…
cs.LG2024
Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng +4
AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the cons…
cs.CL2024
The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias
Kausik Lakkaraju, Aniket Gupta, Biplav Srivastava +2
Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other…