There is a middle ground between parametric and ad-hoc polymorphism in which a computation can depend upon a type parameter but is restricted to being de ned at all types in an inductive fashion. We call such polymorphism non-parametric. We show how nonparametric polymorphism can be used to implement a variety of useful language mechanisms including overloading, unboxed data representations in ...

Fault localization is a major activity in program debugging. To automate this time-consuming task, many existing fault-localization techniques compare passed executions and failed executions, and suggest suspicious program elements, such as predicates or statements, to facilitate the identification of faults. To do that, these techniques propose statistical models and use hypothesis testing met...

Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically verifying how these tests compare with more classical tests, on various conditions. More precisely, using a very large dataset to estimate the w...

The subject of Nonparametric statistics is statistical inference applied to noisy observations of infinite-dimensional “parameters” like images and time-dependent signals. This is a mathematical area on the border between Statistics and Functional Analysis, the latter name taken in its “literal” meaning – as geometry of spaces of functions. What follows is the 8-lecture course given by the auth...

Deep neural networks (DNNs) and probabilistic graphical models (PGMs) are the two main tools for statistical modeling. While DNNs provide the ability to model rich and complex relationships between input and output variables, PGMs provide the ability to encode dependencies among the output variables themselves. End-to-end training methods for models with structured graphical dependencies on top...

Texture is one of the most informative visual cues that help us understand our environment. Texture analysis is an important step in many visual tasks, such as scene segmentation, object recognition, and shape and depth perception. In this chapter we consider the problem of texture recognition and provide an overview of our recent work on this topic ((21, 19, 18]). Our method is based on repres...