Reilly Cannon

Max Vargas, Reilly Cannon, Andrew Engel et al.

Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).

Sinjini Banerjee, Reilly Cannon, Tim Marrinan et al.

Training a deep neural network (DNN) often involves stochastic optimization, which means each run will produce a different model. Several works suggest this variability is negligible when models have the same performance, which in the case of classification is test accuracy. However, models with similar test accuracy may not be computing the same function. We propose a new measure of closeness between classification models based on the output of the network before thresholding. Our measure is based on a robust hypothesis-testing framework and can be adapted to other quantities derived from trained models.

Reilly Cannon, Nicolette M. Laird, Caesar Vazquez et al.

Synthetic tabular data generation has emerged as a promising method to address limited data availability and privacy concerns. With the sharp increase in the performance of large language models in recent years, researchers have been interested in applying these models to the generation of tabular data. However, little is known about the quality of the generated tabular data from large language models. The predominant method for assessing the quality of synthetic tabular data is the train-synthetic-test-real approach, where the artificial examples are compared to the original by how well machine learning models, trained separately on the real and synthetic sets, perform in some downstream tasks. This method does not directly measure how closely the distribution of generated data approximates that of the original. This paper introduces rigorous methods for directly assessing synthetic tabular data against real data by looking at inter-column dependencies within the data. We find that large language models (GPT-2), both when queried via few-shot prompting and when fine-tuned, and GAN (CTGAN) models do not produce data with dependencies that mirror the original real data. Results from this study can inform future practice in synthetic data generation to improve data quality.

Sinjini Banerjee, Tim Marrinan, Reilly Cannon et al.

Deep neural network training often involves stochastic optimization, meaning each run will produce a different model. This implies that hyperparameters of the training process, such as the random seed itself, can potentially have significant influence on the variability in the trained models. Measuring model quality by summary statistics, such as test accuracy, can obscure this dependence. We propose a robust hypothesis testing framework and a novel summary statistic, the $α$-trimming level, to measure model similarity. Applying hypothesis testing directly with the $α$-trimming level is challenging because we cannot accurately describe the distribution under the null hypothesis. Our framework addresses this issue by determining how closely an approximate distribution resembles the expected distribution of a group of individually trained models and using this approximation as our reference. We then use the $α$-trimming level to suggest how many training runs should be sampled to ensure that an ensemble is a reliable representative of the true model performance. We also show how to use the $α$-trimming level to measure model variability and demonstrate experimentally that it is more expressive than performance metrics like validation accuracy, churn, or expected calibration error when taken alone. An application of fine-tuning over random seed in transfer learning illustrates the advantage of our new metric.