Consistent Polyhedral Surrogates for Top-kk Classification and Variants

Top-$k$ classification is a generalization of multiclass classification used widely in information retrieval, image classification, and other extreme classification settings. Several hinge-like (piecewise-linear) surrogates have been proposed for the problem, yet all are either non-convex or inconsistent. For the proposed hinge-like surrogates that are convex (i.e., polyhedral), we apply the recent embedding framework of Finocchiaro et al. (2019; 2022) to determine the prediction problem for which the surrogate is consistent. These problems can all be interpreted as variants of top-$k$ classification, which may be better aligned with some applications. We leverage this analysis to derive constraints on the conditional label distributions under which these proposed surrogates become consistent for top-$k$. It has been further suggested that every convex hinge-like surrogate must be inconsistent for top-$k$. Yet, we use the same embedding framework to give the first consistent polyhedral surrogate for this problem

AdaCAD: Parametric Design as a New Form of Notation for Complex Weaving

Woven textiles are increasingly a medium through which HCI is inventing new technologies. Key challenges in integrating woven textiles in HCI include the high level of textile knowledge required to make effective use of the new possibilities they afford and the need for tools that bridge the concerns of textile designers and concerns of HCI researchers. This paper presents AdaCAD, a parametric design tool for designing woven textile structures. Through our design and evaluation of AdaCAD we found that parametric design helps weavers notate and explain the logics behind the complex structures they generate. We discuss these finding in relation to prior work in integrating craft and/or weaving in HCI, histories of woven notation, and boundary object theory to illuminate further possibilities for collaboration between craftspeople and HCI practitioners