Based on our framework, we also present a hybrid of DRL and EMalgorithms thathasaguaranteed convergencerate.

Machine learning has been quitesuccessful atgenerativemodeling ofcomplexdomains such asimages, audio, and text.

We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.

The paper paper applies Deep Kernel Learning [DKL, 1] to Semi-Supervised Regression. DKL is a combination of a Gaussian Process and a Deep Neural Network (DNN). The idea is to use DNN as a feature transformer inside the kernel of a Gaussian Process (GP). In other words, the the GP operates on the outputs of the DNN. Both the GP and the DNN can be trained using SGD in end-to-end fashion.

This paper investigates the problem of active-semi-supervised clustering, by considering both noiseless (perfect oracle) and noisy (imperfect oracle) query responses. The authors provide probabilistic guarantees for low approximation errors to the true optimal k-means objective. The corresponding query complexities are substantially lower than in the existing literature. Importantly, as noted by the authors, their query complexity is independent of the size of the dataset. The main strength of the paper lies in the considerable technical rigour with which the subject has been handled.

A significant limitation of one-class classification anomaly detection methods is their reliance on the assumption that unlabeled training data only contains normal instances. To overcome this impractical assumption, we propose two novel classification-based anomaly detection methods. Firstly, we introduce a semi-supervised shallow anomaly detection method based on an unbiased risk estimator. Secondly, we present a semi-supervised deep anomaly detection method utilizing a nonnegative (biased) risk estimator. We establish estimation error bounds and excess risk bounds for both risk minimizers. Additionally, we propose techniques to select appropriate regularization parameters that ensure the nonnegativity of the empirical risk in the shallow model under specific loss functions. Our extensive experiments provide strong evidence of the effectiveness of the risk-based anomaly detection methods.

A graph-based prior is proposed for parametric semi-supervised classi- fication. The prior utilizes both labelled and unlabelled data; it also in- tegrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff be- tween the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is per- formed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.

There have been many graph-based approaches for semi-supervised clas- sification. One problem is that of hyperparameter learning: performance depends greatly on the hyperparameters of the similarity graph, trans- formation of the graph Laplacian and the noise model. We present a Bayesian framework for learning hyperparameters for graph-based semi- supervised classification. Given some labeled data, which can contain inaccurate labels, we pose the semi-supervised classification as an in- ference problem over the unknown labels. Expectation Propagation is used for approximate inference and the mean of the posterior is used for classification.

This thesis explores the benefits machine learning algorithms can bring to online planning and scheduling for autonomous vehicles in off-road situations. Mainly, we focus on typical problems of interest which include computing itineraries that meet certain objectives, as well as computing scheduling strategies to execute synchronized maneuvers with other vehicles. We present a range of learning-based heuristics to assist different itinerary planners. We show that these heuristics allow a significant increase in performance for optimal planners. Furthermore, in the case of approximate planning, we show that not only does the running time decrease, the quality of the itinerary found also becomes almost always better. Finally, in order to synthesize strategies to execute synchronized maneuvers, we propose a novel type of scheduling controllability and a learning-assisted algorithm. The proposed framework achieves significant improvement on known benchmarks in this controllability type over the performance of state-of-the-art works in a related controllability type. Moreover, it is able to find strategies on complex scheduling problems for which previous works fail to do so.

Much of the recent progress made in node classification on graphs can be credited to the careful design on graph neural networks (GNN) and label propagation algorithms. However, in the literature, in addition to improvements to the model architecture, there are a number of improvements either briefly mentioned as implementation details or visible only in source code, and these overlooked techniques may play a pivotal role in their practical use. In this paper, we first summarize a collection of existing refinements, and then propose several novel techniques regarding these model designs and label usage. We empirically evaluate their impacts on the final model accuracy through ablation studies, and show that we are able to significantly improve various GNN models to the extent that they outweigh the gains from model architecture improvement. Notably, many of the top-ranked models on Open Graph Benchmark benefit from our techniques.