Subjectivity in inductive inference

This paper examines circumstances under which subjectivity enhances the effectiveness of inductive reasoning. We consider agents facing a data generating process who are characterized by inference rules that may be purely objective (or data-based) or may incorporate subjective considerations. The basic intuition is that agents who invoke no subjective considerations are doomed to "overfit" the data and therefore engage in ineffective learning. The analysis places no computational or memory limitations on the agents|the role for subjectivity emerges in the presence of unlimited reasoning powers.Inductive inference, simplicity, prediction, learning

A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research

Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies

Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.Comment: Paper under revie

GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials

Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: over-smoothing, over-fitting, difficult to train, and strong homophily assumption. For example, Simple Graph Convolution (SGC) is known to suffer from the first and fourth limitations. To tackle these limitations, we identify a set of key designs including (D1) dilated convolution, (D2) multi-channel learning, (D3) self-attention score, and (D4) sign factor to boost learning from different types (i.e. homophily and heterophily) and scales (i.e. small, medium, and large) of networks, and combine them into a graph neural network, GPNet, a simple and efficient one-layer model. We theoretically analyze the model and show that it can approximate various graph filters by adjusting the self-attention score and sign factor. Experiments show that GPNet consistently outperforms baselines in terms of average rank, average accuracy, complexity, and parameters on semi-supervised and full-supervised tasks, and achieves competitive performance compared to state-of-the-art model with inductive learning task.Comment: 15 pages, 15 figure

Assessing the probability of patients reoffending after discharge from low to medium secure forensic mental health services: An inductive prevention paradox

Citizens of developed societies are troubled by those who commit ‘irrational' crimes against the person. Reoffending by ex-patients following their release from secure mental health services triggers particularly intense angst when amplified by media and political scrutiny. Forensic mental health service providers are expected to minimise the occurrence of such transgressions by releasing only those patients who are judged acceptably unlikely to reoffend. However, reoffending probabilities can only be estimated by observing behaviour in secure institutional settings designed specifically to prevent patients from transgressing. The article explores this ‘inductive prevention paradox' which arises when the implementation of measures designed to avoid an adverse event obscures direct observation of what might have happened if prophylaxis had not been attempted. The analysis presented draws on data obtained in 1999–2003 from two qualitative studies in medium to low secure UK institutions, one providing forensic mental health services and the other forensic learning disability services. We explored the views of 56 staff members and 21 patients about risk management in forensic services and undertook additional 25 staff interviews for case studies of the 21 patients. The wider applicability of the inductive prevention paradox will be considered in the Discussion. We argue that the prognostic limitations arising from prevention have been underestimated by policy makers and in official inquiries; and that the prevailing personal risk assessment framework needs to be complemented by greater attention to the environments which patients will be discharged into

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur