Normalizing Flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions, but also deliver density estimation by construction. We propose here an in-depth comparison of coupling and autoregressive flows, both of the affine and rational quadratic spline type, considering four different architectures: Real-valued Non-Volume Preserving (RealNVP), Masked Autoregressive Flow (MAF), Coupling Rational Quadratic Spline (C-RQS), and Autoregressive Rational Quadratic Spline (A-RQS). We focus on different target distributions of increasing complexity with dimensionality ranging from 4 to 1000. The performances are discussed in terms of different figures of merit: the one-dimensional Wasserstein distance, the one-dimensional Kolmogorov-Smirnov test, the Frobenius norm of the difference between correlation matrices, and the training time. Our results indicate that the A-RQS algorithm stands out both in terms of accuracy and training speed. Nonetheless, all the algorithms are generally able, without much fine-tuning, to learn complex distributions with limited training data and in a reasonable time, of the order of hours on a Tesla V100 GPU. The only exception is the C-RQS, which takes significantly longer to train, and does not always provide good accuracy. All algorithms have been implemented using TensorFlow2 and TensorFlow Probability and made available on GitHub.

Existing active strategies for training surrogate models yield accurate structural reliability estimates by aiming at design space regions in the vicinity of a specified limit state function. In many practical engineering applications, various damage conditions, e.g. repair, failure, should be probabilistically characterized, thus demanding the estimation of multiple performance functions. In this work, we investigate the capability of active learning approaches for efficiently selecting training samples under a limited computational budget while still preserving the accuracy associated with multiple surrogated limit states. Specifically, PC-Kriging-based surrogate models are actively trained considering a variance correction derived from leave-one-out cross-validation error information, whereas the sequential learning scheme relies on U-function-derived metrics. The proposed active learning approaches are tested in a highly nonlinear structural reliability setting, whereas in a more practical application, failure and repair events are stochastically predicted in the aftermath of a ship collision against an offshore wind substructure. The results show that a balanced computational budget administration can be effectively achieved by successively targeting the specified multiple limit state functions within a unified active learning scheme.

Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knockouts. However, the targets of the interventions are often uncertain or unknown and the number of observations limited. As a result, standard causal discovery methods can no longer be reliably used. To fill this gap, we propose a Bayesian framework (BaCaDI) for discovering and reasoning about the causal structure that underlies data generated under various unknown experimental or interventional conditions. BaCaDI is fully differentiable, which allows us to infer the complex joint posterior over the intervention targets and the causal structure via efficient gradient-based variational inference. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.

In sponsored search advertising (SSA), keywords serve as the basic unit of business model, linking three stakeholders: consumers, advertisers and search engines. This paper presents an overarching framework for keyword decisions that highlights the touchpoints in search advertising management, including four levels of keyword decisions, i.e., domain-specific keyword pool generation, keyword targeting, keyword assignment and grouping, and keyword adjustment. Using this framework, we review the state-of-the-art research literature on keyword decisions with respect to techniques, input features and evaluation metrics. Finally, we discuss evolving issues and identify potential gaps that exist in the literature and outline novel research perspectives for future exploration.

Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of {over-smoothing} and {under-reaching} to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, {mis-simplification}, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines.

In the field of health-care and bio-medical research, understanding the relationship between the symptoms of diseases is crucial for early diagnosis and determining hidden relationships between diseases. The study aimed to understand the extent of symptom types in disease prediction tasks. In this research, we analyze a pre-generated symptom-based human disease dataset and demonstrate the degree of predictability for each disease based on the Convolutional Neural Network and the Support Vector Machine. Ambiguity of disease is studied using the K-Means and the Principal Component Analysis. Our results indicate that machine learning can potentially diagnose diseases with the 98-100% accuracy in the early stage, taking the characteristics of symptoms into account. Our result highlights that types of unusual symptoms are a good proxy for disease early identification accurately. We also highlight that unusual symptoms increase the accuracy of the disease prediction task.

Probabilistic circuits (PCs) are a prominent representation of probability distributions with tractable inference. While parameter learning in PCs is rigorously studied, structure learning is often more based on heuristics than on principled objectives. In this paper, we develop Bayesian structure scores for deterministic PCs, i.e., the structure likelihood with parameters marginalized out, which are well known as rigorous objectives for structure learning in probabilistic graphical models. When used within a greedy cutset algorithm, our scores effectively protect against overfitting and yield a fast and almost hyper-parameter-free structure learner, distinguishing it from previous approaches. In experiments, we achieve good trade-offs between training time and model fit in terms of log-likelihood. Moreover, the principled nature of Bayesian scores unlocks PCs for accommodating frameworks such as structural expectation-maximization.

We consider a task of surveillance-evading path-planning in a continuous setting. An Evader strives to escape from a 2D domain while minimizing the risk of detection (and immediate capture). The probability of detection is path-dependent and determined by the spatially inhomogeneous surveillance intensity, which is fixed but a priori unknown and gradually learned in the multi-episodic setting. We introduce a Bayesian reinforcement learning algorithm that relies on a Gaussian Process regression (to model the surveillance intensity function based on the information from prior episodes), numerical methods for Hamilton-Jacobi PDEs (to plan the best continuous trajectories based on the current model), and Confidence Bounds (to balance the exploration vs exploitation). We use numerical experiments and regret metrics to highlight the significant advantages of our approach compared to traditional graph-based algorithms of reinforcement learning.

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.

With the accelerated technology adoption by a growing number of users, social media have become the main medium for the dissemination of information on current news and events. While these new media bring several benefits (e.g., a large number of consumers reached, instant and continuous updates on one's topics of interest), they also enable the spread of harmful information in the form of fake news, and may thus polarize public discourse regarding critical topics (e.g., elections [32], vaccination [30], health hazards [24]) and threaten democratic values [35]. Because of its detrimental effects on society at large, the fake news phenomenon has been studied by scientists and practitioners alike; fake news is defined as news articles that intentionally contain verifiably false misleading information inconsistent with factual reality [2, 23, 46, 10, 4, 13, 43]. To mitigate the threat of fake news, journalists have started to manually classify news and offer websites with fact-checking mechanisms that provide a verdict regarding its veracity, such as PolitiFact (https://www.politifact.com/)