1 citations · 1 across the 5 of their papers we have counts for
5 papers
Amortizing Scaling Law Construction Costs
Abhash Kumar Jha, Diana Alexandra Onuţu, Neeratyoy Mallik +6
Scaling laws guide the design choices for training large foundation models, but deriving them involves training an exhaustive grid over hyperparameters, token budgets, and paramete…
Interpreting Microbiome Relative Abundance Data Using Symbolic Regression
Swagatam Haldar, Christoph Stein-Thoeringer, Vadim Borisov
Understanding the complex interactions within the microbiome is crucial for developing effective diagnostic and therapeutic strategies. Traditional machine learning models often la…
Trust Regions for Explanations via Black-Box Probabilistic Certification
Amit Dhurandhar, Swagatam Haldar, Dennis Wei +1
Given the black box nature of machine learning models, a plethora of explainability methods have been developed to decipher the factors behind individual decisions. In this paper,…
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model…
Automated Testing of AI Models
Swagatam Haldar, Deepak Vijaykeerthy, Diptikalyan Saha
The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we too…