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Universal Inference for model selection on networks
Eric Yanchenko, Jonathan P. Williams, Ryan Martin
Model selection and hypothesis testing are important tasks on networks. A key challenge lies in the inherent dependence in network data, as well as the fact that typically only a s…
Hypothesis testing for community structure in temporal networks using e-values
Eric Yanchenko, Jonathan P. Williams, Ryan Martin
Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community st…
Generalized Universal Inference on Risk Minimizers
Neil Dey, Ryan Martin, Jonathan P. Williams
A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditiona…
Multiple Testing in Generalized Universal Inference
Neil Dey, Ryan Martin, Jonathan P. Williams
Compared to p-values, e-values provably guarantee safe, valid inference. If the goal is to test multiple hypotheses simultaneously, one can construct e-values for each individual t…