"Generalized Aliasing Decomposition completely explains non-monotonic risk curves in regression"
Wednesday, Oct 28, 2026 
- Colloquium - 499 DSL Seminar Room
- 03:30 to 04:30 PM Eastern Time (US and Canada)
Abstract:
A central problem in data science is to use potentially noisy samples of an unknown function to predict function values for unseen inputs. In classical statistics, the predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, overparametrized models exhibit counterintuitive behaviors, such as “double descent” in which models of increasing complexity exhibit decreasing generalization error. Other models may exhibit more complicated patterns of predictive error with multiple peaks and valleys. Neither double descent nor multiple descent phenomena are well explained by the bias-variance decomposition. We introduce a decomposition that we call the generalized aliasing decomposition (GAD) to explain the relationship between predictive performance and model complexity. The GAD decomposes the predictive error into three parts: (1) model insufficiency, which dominates when the number of parameters is much smaller than the number of data points, (2) data insufficiency, which dominates when the number of parameters is much greater than the number of data points, and (3) generalized aliasing, which dominates between these two extremes. We demonstrate the applicability of the GAD to diverse applications, including random feature models from machine learning, Fourier transforms from signal processing, solution methods for differential equations, and predictive formation enthalpy in materials discovery. Because key components of the generalized aliasing decomposition can be explicitly calculated from the relationship between model class and samples without seeing any data labels, it can answer questions related to experimental design and model selection before collecting data or performing experiments. We further demonstrate this approach on several examples and discuss implications for predictive modeling and data science.
Speaker Biography:
Jared received his Phd in Applied and Interdisciplinary Mathematics from the University of Michigan with two doctoral advisors: one a theoretical physicist, and the other an atmospheric scientist. After completing a postdoctoral position at Los Alamos National Laboratory in ocean modeling, he has been in the mathematics department at Brigham Young University ever since. His current research is primarily focused on data-driven techniques for dynamical systems with applications in cloud physics, engineering, natural hazards, and fluid mechanics. In his spare time, Jared loves being outdoors in the Rocky mountains and tries to spend as much time as possible hiking, trail running, mountain biking, and skiing.
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