6 citations · 15 across the 16 of their papers we have counts for
4 papers · 1 filter
On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating
Michael Widener, Kausik Lakkaraju, John Aydin +1
Time-series forecasting models (TSFM) have evolved from classical statistical methods to sophisticated foundation models, yet understanding why and when these models succeed or fai…
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…
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON
Vansh Nagpal, Siva Likitha Valluru, Kausik Lakkaraju +4
A common decision made by people, whether healthy or with health conditions, is choosing meals like breakfast, lunch, and dinner, comprising combinations of foods for appetizer, ma…
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…