Link prediction problem has increasingly become prominent in many domains such as social network analyses, bioinformatics experiments, transportation networks, criminal investigations and so forth. A variety of techniques has been developed for link prediction problem, categorized into 1) similarity based approaches which study a set of features to extract similar nodes; 2) learning based approaches which extract patterns from the input data; 3) probabilistic statistical approaches which optimize a set of parameters to establish a model which can best compute formation probability. However, existing literatures lack approaches which utilize strength of each approach by integrating them to achieve a much more productive one. To tackle the link prediction problem, we propose an approach based on the combination of first and second group methods; the existing studied works use just one of these categories. Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes, which enforce the approach more efficiency compared to approaches using mere measures. Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures which differentiate the strength of clusters; basically, the usage of local and global indices and the clustering information plays an imperative role in our link prediction process. Some extensive experiments held on real datasets including Facebook, Brightkite and HepTh indicate good performances of our proposal method. Besides, we have experimentally verified our approach with some previous techniques in the area to prove the supremacy of ours.

Artificial Intelligence (AI) has found many applications in medicine [15] and, more specifically, in cancer research [32] in the form of predictive models for diagnosis [14], prognosis [6] and therapy planning [12]. As a subfield of AI, Machine Learning (ML) and in particular Deep Learning (DL) has achieved significant results, especially in image processing [3]. Nonetheless, ML and DL models have limited explainability [13] because of their black-box design, which limits their adoption in the clinical field: clinicians and physicians are reluctant to include models that are not transparent in their decision process [24]. While recent research on Explainable AI (XAI) [11] has attacked this problem, DL models are still opaque and difficult to interpret. In contrast, in Probabilistic Graphical Models (PGMs) the interactions between different variables are encoded explicitly: the joint probability distribution P of the variables of interest factorizes according to a graph G, hence the "graphical" connotation. Bayesian Networks (BNs) [23], which we will describe in Section 3.1, are an instance of PGMs that can be used as causal models. In turn, this makes them ideal to use as decision support systems and overcome the limitations of the predictions based on probabilistic associations produced by other ML models [1, 19].

As the rapid development of Intelligent Tutoring Systems (ITS) in the past decade, tracing the students' knowledge state has become more and more important in order to provide individualized learning guidance. This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Plenty of KT models have been proposed and have shown remarkable performance recently. However, the majority of these models use concepts to index questions, which means the predefined skill tags for each question are required in advance to indicate the KCs needed to answer that question correctly. This makes it pretty hard to apply on large-scale online education platforms where questions are often not well-organized by skill tags. In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an end-to-end style model that is able to apply the attentive method to scenes where no predefined skill tags are available without sacrificing its performance. With a novel hybrid embedding method based on the q-matrix and Rasch model, QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Meanwhile, the architecture of QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically validated that our model shows similar or even better performance than state-of-the-art KT methods. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.

As Artificial Intelligence, and Deep Learning in particular, reach impressive results, it gains also unprecedented popularity not only in academics and industry but also in popular culture and society in general. This increasingly ubiquitous AI presence has arisen several concerns about its impacts on humanity and the planet, with some well-known scientists like Stephen Hawking having spoken concerns about AI's accountability [1]. Despite achieving outstanding results in Computer Vision, Natural Language Processing and Game Playing [2, 3], tasks in which AIs formerly have poor performance compared to humans, those concerns about AI triggered debates among research communities, including those discussed by Gary Marcus [4] and on AAAI-2020 debate with Geoffrey Hinton, Yoshua Bengio and Yann LeCun [5].

This paper addresses the $\epsilon$-close parameter tuning problem for Bayesian Networks (BNs): find a minimal $\epsilon$-close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid. Based on the state-of-the-art "region verification" techniques for parametric Markov chains, we propose an algorithm whose capabilities go beyond any existing techniques. Our experiments show that $\epsilon$-close tuning of large BN benchmarks with up to 8 parameters is feasible. In particular, by allowing (i) varied parameters in multiple CPTs and (ii) inter-CPT parameter dependencies, we treat subclasses of parametric BNs that have received scant attention so far.

We introduce Reprompting, an iterative sampling algorithm that searches for the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, we infer CoT recipes that work consistently well for a set of training samples. Our method iteratively samples new recipes using previously sampled solutions as parent prompts to solve other training problems. On five Big-Bench Hard tasks that require multi-step reasoning, Reprompting achieves consistently better performance than the zero-shot, few-shot, and human-written CoT baselines. Reprompting can also facilitate transfer of knowledge from a stronger model to a weaker model leading to substantially improved performance of the weaker model. Overall, Reprompting brings up to +17 point improvements over the previous state-of-the-art method that uses human-written CoT prompts.

We investigate the extent to which offline demonstration data can improve online learning. It is natural to expect some improvement, but the question is how, and by how much? We show that the degree of improvement must depend on the quality of the demonstration data. To generate portable insights, we focus on Thompson sampling (TS) applied to a multi-armed bandit as a prototypical online learning algorithm and model. The demonstration data is generated by an expert with a given competence level, a notion we introduce. We propose an informed TS algorithm that utilizes the demonstration data in a coherent way through Bayes' rule and derive a prior-dependent Bayesian regret bound. This offers insight into how pretraining can greatly improve online performance and how the degree of improvement increases with the expert's competence level. We also develop a practical, approximate informed TS algorithm through Bayesian bootstrapping and show substantial empirical regret reduction through experiments.

We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain the efficiency and efficacy of deep graph networks with the additional advantages of a purely probabilistic model. We show the model's competitiveness on scarce supervision scenarios, handling missing data, and graph classification in comparison to popular neural models. We complement the experiments with qualitative analyses on hyper-parameters and the model's ability to answer probabilistic queries.

Bayesian networks (BN) are directed acyclic graphical (DAG) models that have been adopted into many fields for their strengths in transparency, interpretability, probabilistic reasoning, and causal modeling. Given a set of data, one hurdle towards using BNs is in building the network graph from the data that properly handles dependencies, whether correlated or causal. In this paper, we propose an initial methodology for discovering network structures using Tsetlin Machines.

Understanding the causal relationships among the variables of a system is paramount to explain and control its behaviour. Inferring the causal graph from observational data without interventions, however, requires a lot of strong assumptions that are not always realistic. Even for domain experts it can be challenging to express the causal graph. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks. Existing metrics provide an absolute number of inconsistencies between the graph and the observed data, and without a baseline, practitioners are left to answer the hard question of how many such inconsistencies are acceptable or expected. Here, we propose a novel consistency metric by constructing a surrogate baseline through node permutations. By comparing the number of inconsistencies with those on the surrogate baseline, we derive an interpretable metric that captures whether the DAG fits significantly better than random. Evaluating on both simulated and real data sets from various domains, including biology and cloud monitoring, we demonstrate that the true DAG is not falsified by our metric, whereas the wrong graphs given by a hypothetical user are likely to be falsified.