1 citations · 1 across the 3 of their papers we have counts for
7 papers
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…
GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs
Nitin Gupta, Pallav Koppisetti, Kausik Lakkaraju +1
The rapid proliferation of Generative AI (GenAI) into diverse, high-stakes domains necessitates robust and reproducible evaluation methods. However, practitioners often resort to a…
Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making
Kausik Lakkaraju, Siva Likitha Valluru, Biplav Srivastava
As AI systems increasingly mediate decisions in domains such as credit scoring and financial forecasting, their lack of transparency and bias raises critical concerns for fairness…
FABLE: A Novel Data-Flow Analysis Benchmark on Procedural Text for Large Language Model Evaluation
Vishal Pallagani, Nitin Gupta, John Aydin +1
Understanding how data moves, transforms, and persists, known as data flow, is fundamental to reasoning in procedural tasks. Despite their fluency in natural and programming langua…
SafeChat: A Framework for Building Trustworthy Collaborative Assistants and a Case Study of its Usefulness
Biplav Srivastava, Kausik Lakkaraju, Nitin Gupta +3
Collaborative assistants, or chatbots, are data-driven decision support systems that enable natural interaction for task completion. While they can meet critical needs in modern so…
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…