1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
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…
Valid Inference for Machine Learning Model Parameters
Neil Dey, Jonathan P. Williams
The parameters of a machine learning model are typically learned by minimizing a loss function on a set of training data. However, this can come with the risk of overtraining; in o…
Word Embeddings as Statistical Estimators
Neil Dey, Matthew Singer, Jonathan P. Williams +1
Word embeddings are a fundamental tool in natural language processing. Currently, word embedding methods are evaluated on the basis of empirical performance on benchmark data sets,…
Robust Coordinate Ascent Variational Inference with Markov chain Monte Carlo simulations
Neil Dey, Emmett B. Kendall
Variational Inference (VI) is a method that approximates a difficult-to-compute posterior density using better behaved distributional families. VI is an alternative to the already…
Conformal prediction for text infilling and part-of-speech prediction
Neil Dey, Jing Ding, Jack Ferrell +4
Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventiona…