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cs.LG2024
Understanding Generative AI Content with Embedding Models
Max Vargas, Reilly Cannon, Andrew Engel +2
Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representatio…
cs.LG2023
Efficient kernel surrogates for neural network-based regression
Saad Qadeer, Andrew Engel, Amanda Howard +4
Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitione…
cs.LG2023
Foundation Model's Embedded Representations May Detect Distribution Shift
Max Vargas, Adam Tsou, Andrew Engel +1
Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a mo…