284 citations · 535 across the 21 of their papers we have counts for
22 papers
LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
Chacha Chen, Matthew Jörke, Adam Goliński +4
Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct answers, but also represent and…
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
Eray Erturk, Fahad Kamran, Salar Abbaspourazad +6
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been…
Breiman's two cultures: You don't have to choose sides
Andrew C. Miller, Nicholas J. Foti, Emily B. Fox
Breiman's classic paper casts data analysis as a choice between two cultures: data modelers and algorithmic modelers. Stated broadly, data modelers use simple, interpretable models…
Learning Insulin-Glucose Dynamics in the Wild
Andrew C. Miller, Nicholas J. Foti, Emily Fox
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics d…
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Christopher Aicher, Nicholas J. Foti, Emily B. Fox
Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncat…
Stochastic Gradient MCMC for State Space Models
Christopher Aicher, Yi-An Ma, Nicholas J. Foti +1
State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic…