4 citations · 4 across the 3 of their papers we have counts for
4 papers
Poor starting points in machine learning
Mark Tygert
Poor (even random) starting points for learning/training/optimization are common in machine learning. In many settings, the method of Robbins and Monro (online stochastic gradient…
Significance testing without truth
William Perkins, Mark Tygert, Rachel Ward
A popular approach to significance testing proposes to decide whether the given hypothesized statistical model is likely to be true (or false). Statistical decision theory provides…
Computing the asymptotic power of a Euclidean-distance test for goodness-of-fit
William Perkins, Gary Simon, Mark Tygert
A natural (yet unconventional) test for goodness-of-fit measures the discrepancy between the model and empirical distributions via their Euclidean distance (or, equivalently, via i…
A comparison of the discrete Kolmogorov-Smirnov statistic and the Euclidean distance
Jacob Carruth, Mark Tygert, Rachel Ward
Goodness-of-fit tests gauge whether a given set of observations is consistent (up to expected random fluctuations) with arising as independent and identically distributed (i.i.d.)…