1 citations · 2 across the 13 of their papers we have counts for
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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…
On Learning with LAD
C. A. Jothishwaran, Biplav Srivastava, Jitin Singla +1
The logical analysis of data, LAD, is a technique that yields two-class classifiers based on Boolean functions having disjunctive normal form (DNF) representation. Although LAD alg…