Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

End-to-End Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.Comment: NIPS 2017 camera-ready, NIPS 201

Asymptotically rigid mapping class groups and Thompson's groups

We consider Thompson's groups from the perspective of mapping class groups of surfaces of infinite type. This point of view leads us to the braided Thompson groups, which are extensions of Thompson's groups by infinite (spherical) braid groups. We will outline the main features of these groups and some applications to the quantization of Teichm\"uller spaces. The chapter provides an introduction to the subject with an emphasis on some of the authors results.Comment: survey 77

Correlates of War and Sino Revisionism

Some conflict resolution and national security professionals contend there is no shortfall of qualitative research on Asia, China, Realism, or war. That said, this doctoral dissertation has two overarching purposes: first, an empirical study of interstate conflict at the regional and systematic levels. Second, is to examine the extent to which the Correlates of War covariates associated with the People’s Republic of China’s revisionist strategies. Combining these objectives led to the formulation of the main research question: What is the relationship between the correlates of war and China’s revisionist strategy in Asia? Realism contends that strong national capability and displays of military resolve guarantees safety. A counterargument, provided by the steps-to-war (STW) theory, is these power politics increases the chances of war. This dissertation empirically explores the latter by examination of 461 dispute dyads that cover a fifty-year period, an inductive statistical method was used to determine the relationship between the correlates of war and China’s revisionism. Although there were several research findings, three are indeed salient: 1) There was no statistical evidence that the STW theory, territorial revisions, rivalry, alliance-making, or arms races increase war onset chances; 2) When war onset was substituted for low-intensity interstate violence as an outcome variable, there was strong empirical evidence that the Correlates of War were statistical significant; and 3) China was essentially interstate war-adverse, but violence-prone. In short, this scientific study of war expanded the correlation knowledge of war onset, particularly in Asia