The Funder and the Intermediary, in Support of the Artist: A Look at Rationales, Roles, and Relationships

This article, examining the ecology of funders' use of intermediaries and regranting organizations, came about as a direct offshoot of GIA's Research Initiative on Support for Individual Artists, begun in 2011. As the research team worked to map the pathways that support followed from funder to artist, a complex map of options and routes began to emerge, and intermediaries and regranters were often part of that picture. It became increasingly clear that this was an essential and important part of the overall system. It also emerged that this was an area of philanthropic practice that had been little examined, and about which little had been published. Interviews with funders during the research work also revealed that while a number of foundations were using intermediaries, their practices had independently evolved, and a wide range of methods and procedures were in use. What follows is the first tangible product of GIA's Research Initiative on Support for Individual Artists. In her analysis, Claudia Bach provides both an overview of the range of philanthropic practices involving intermediaries and regranters, as well as an exploration of a number of related topics and questions that emerged during the course of this work

Social Categories are Natural Kinds, not Objective Types (and Why it Matters Politically)

There is growing support for the view that social categories like men and women refer to “objective types” (Haslanger 2000, 2006, 2012; Alcoff 2005). An objective type is a similarity class for which the axis of similarity is an objective rather than nominal or fictional property. Such types are independently real and causally relevant, yet their unity does not derive from an essential property. Given this tandem of features, it is not surprising why empirically-minded researchers interested in fighting oppression and marginalization have found this ontological category so attractive: objective types have the ontological credentials to secure the reality (and thus political representation) of social categories, and yet they do not impose exclusionary essences that also naturalize and legitimize social inequalities. This essay argues that, from the perspective of these political goals of fighting oppression and marginalization, the category of objective types is in fact a Trojan horse; it looks like a gift, but it ends up creating trouble. I argue that objective type classifications often lack empirical adequacy, and as a result they lack political adequacy. I also provide, and in reference to the normative goals described above, several arguments for preferring a social ontology of natural kinds with historical essences

Graph kernels between point clouds

Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and practical constraints associated with point clouds in computer vision and graphics. In this paper, we present extensions of graph kernels for point clouds, which allow to use kernel methods for such ob jects as shapes, line drawings, or any three-dimensional point clouds. In order to design rich and numerically efficient kernels with as few free parameters as possible, we use kernels between covariance matrices and their factorizations on graphical models. We derive polynomial time dynamic programming recursions and present applications to recognition of handwritten digits and Chinese characters from few training examples

Convex Analysis and Optimization with Submodular Functions: a Tutorial

Set-functions appear in many areas of computer science and applied mathematics, such as machine learning, computer vision, operations research or electrical networks. Among these set-functions, submodular functions play an important role, similar to convex functions on vector spaces. In this tutorial, the theory of submodular functions is presented, in a self-contained way, with all results shown from first principles. A good knowledge of convex analysis is assumed

Sharp analysis of low-rank kernel matrix approximations

We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading to infinite-dimensional feature spaces, a common practical limiting difficulty is the necessity of computing the kernel matrix, which most frequently leads to algorithms with running time at least quadratic in the number of observations n, i.e., O(n^2). Low-rank approximations of the kernel matrix are often considered as they allow the reduction of running time complexities to O(p^2 n), where p is the rank of the approximation. The practicality of such methods thus depends on the required rank p. In this paper, we show that in the context of kernel ridge regression, for approximations based on a random subset of columns of the original kernel matrix, the rank p may be chosen to be linear in the degrees of freedom associated with the problem, a quantity which is classically used in the statistical analysis of such methods, and is often seen as the implicit number of parameters of non-parametric estimators. This result enables simple algorithms that have sub-quadratic running time complexity, but provably exhibit the same predictive performance than existing algorithms, for any given problem instance, and not only for worst-case situations

Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning

For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depends on the number of observations. This is usually done through the penalization of predictor functions by Euclidean or Hilbertian norms. In this paper, we explore penalizing by sparsity-inducing norms such as the l1-norm or the block l1-norm. We assume that the kernel decomposes into a large sum of individual basis kernels which can be embedded in a directed acyclic graph; we show that it is then possible to perform kernel selection through a hierarchical multiple kernel learning framework, in polynomial time in the number of selected kernels. This framework is naturally applied to non linear variable selection; our extensive simulations on synthetic datasets and datasets from the UCI repository show that efficiently exploring the large feature space through sparsity-inducing norms leads to state-of-the-art predictive performance

Structured sparsity-inducing norms through submodular functions

Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection problem is often turned into a convex optimization problem by replacing the cardinality function by its convex envelope (tightest convex lower bound), in this case the L1-norm. In this paper, we investigate more general set-functions than the cardinality, that may incorporate prior knowledge or structural constraints which are common in many applications: namely, we show that for nondecreasing submodular set-functions, the corresponding convex envelope can be obtained from its \lova extension, a common tool in submodular analysis. This defines a family of polyhedral norms, for which we provide generic algorithmic tools (subgradients and proximal operators) and theoretical results (conditions for support recovery or high-dimensional inference). By selecting specific submodular functions, we can give a new interpretation to known norms, such as those based on rank-statistics or grouped norms with potentially overlapping groups; we also define new norms, in particular ones that can be used as non-factorial priors for supervised learning

Gender Is a Natural Kind with a Historical Essence

Traditional debate on the metaphysics of gender has been a contrast of essentialist and social-constructionist positions. The standard reaction to this opposition is that neither position alone has the theoretical resources required to satisfy an equitable politics. This has caused a number of theorists to suggest ways in which gender is unified on the basis of social rather than biological characteristics but is “real” or “objective” nonetheless – a position I term social objectivism. This essay begins by making explicit the motivations for, and central assumptions of, social objectivism. I then propose that gender is better understood as a real kind with a historical essence, analogous to the biologist’s claim that species are historical entities. I argue that this proposal achieves a better solution to the problems that motivate social objectivism. Moreover, the account is consistent with a post-positivist understanding of the classificatory practices employed within the natural and social sciences