5 papers
In-Context Learning Under Regime Change
Carson Dudley, Yutong Bi, Xiaofeng Liu +1
Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models must detect the change, disca…
Learning From Simulators: A Theory of Simulation-Grounded Learning
Carson Dudley, Marisa Eisenberg
Simulation-Grounded Neural Networks (SGNNs) are predictive models trained entirely on synthetic data from mechanistic simulations. They have achieved state-of-the-art performance i…
Not All Accuracy Is Equal: Prioritizing Independence in Infectious Disease Forecasting
Carson Dudley, Marisa Eisenberg
Ensemble forecasts have become a cornerstone of large-scale disease response, underpinning decision making at agencies such as the US Centers for Disease Control and Prevention (CD…
Mantis: A Foundation Model for Mechanistic Disease Forecasting
Carson Dudley, Reiden Magdaleno, Christopher Harding +3
Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, a…
Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery
Carson Dudley, Reiden Magdaleno, Christopher Harding +1
Scientific modeling faces a tradeoff between the interpretability of mechanistic theory and the predictive power of machine learning. While existing hybrid approaches have made pro…