Multimodal fusion is important for building intelligent systems that exploit patterns across diverse data sources for improved decision-making. However, the reliability and robustness of these systems in safety-critical domains are often compromised by the inherent noise and incompleteness of data. Probabilistic Circuits (PCs) have recently emerged as a promising approach for late (or decision) fusion. Their strength lies in being both expressive and capable of inferring source credibility due to their ability to tractably perform exact probabilistic inference. However, their ability to handle missing data and their reliability in practical scenarios remains underexplored. This work investigates the robustness of PCs as fusion functions in scenarios with missing and noisy data; particularly by examining their impact on the calibration and reliability of the resulting classifiers. Our findings show that PCs not only enable the modeling of complex correlations across modalities but also lead to calibrated and reliable classifiers, highlighting their potential as a robust fusion mechanism in multimodal systems.

Information fusion includes the integration of data for situational understanding. As a situation unfolds, maintaining awareness depends on diverse collections of data. In complex and dynamic scenarios, human operators face the difficult task of choosing which data to collect next. Hence, there is a need for multilayered fusion processes that exploit multiple models and levels of abstraction for understanding and sense-making Data collection has its roots in sensor management; however, there is an analogous need for data management - such as the incorporation of public domain data. Mature sensor management includes methods to utilize platform, sensor, and scene modeling so as to guide the user for future data collection. Additionally, these physics-based models could be a method to guide human-derived information models. Using the Data Fusion Information Group Model (DFIG), we develop an equivalent method for diffusion control. This paper focuses on recent techniques in statistical relational learning (SRL), Markov logic networks (MLN), and ontologies to support the control diffusion of data sensing to answer user queries.

One of the key advantages of Inductive Logic Programming systems is the ability of the domain experts to provide background knowledge as modes that allow for efficient search through the space of hypotheses. However, there is an inherent assumption that this expert should also be an ILP expert to provide effective modes. We relax this assumption by designing a graphical user interface that allows the domain expert to interact with the system using Entity Relationship diagrams. These interactions are used to construct modes for the learning system. We evaluate our algorithm on a probabilistic logic learning system where we demonstrate that the user is able to construct effective background knowledge on par with the expert-encoded knowledge on five data sets.

Over the past decade, exploiting relations and symmetries within probabilistic models has been proven to be surprisingly effective at solving large scale data mining problems. One of the key operations inside these lifted approaches is counting - be it for parameter/structure learning or for efficient inference. Typically, however, they just count exploiting the logical structure using adhoc operators. This paper investigates whether ‘Compilation to Graph Databases’ could be a practical technique for scaling lifted probabilistic inference and learning methods. We demonstrate that the proposed approach achieves reasonable speed-ups for both inference and learning, without sacrificing performance.

Transfer learning is typically performed between problem instances within the same domain. We consider the problem of transferring across domains. To this effect, we adopt a probabilistic logic approach. First, our approach automatically identifies predicates in the target domain that are similar in their relational structure to predicates in the source domain. Second, it transfers the logic rules and learns the parameters of the transferred rules using target data. Finally, it refines the rules as necessary using theory refinement. Our experimental evidence supports that this transfer method finds models as good or better than those found with state-of-the-art methods, with and without transfer, and in a fraction of the time.

We consider the problem of learning probabilistic models from relational data. One of the key issues with relational data is class imbalance where the number of negative examples far outnumbers the number of positive examples. The common approach for dealing with this problem is the use of sub-sampling of negative examples. We, on the other hand, consider a soft margin approach that explicitly trades off between the false positives and false negatives. We apply this approach to the recently successful formalism of relational functional gradient boosting. Specifically, we modify the objective function of the learning problem to explicitly include the trade-off between false positives and negatives. We show empirically that this approach is more successful in handling the class imbalance problem than the original framework that weighed all the examples equally.

Improvements in medical imaging techniques have provided clinicians the ability to obtain detailed brain images of patients at lower costs. This increased availability of rich data opens up new avenues of research that promise better understanding of common brain ailments such as Alzheimer's Disease and dementia. Improved data mining techniques, however, are required to leverage these new data sets to identify intermediate disease states (e.g., mild cognitive impairment) and perform early diagnosis. We propose a graphical model framework based on conditional random fields (CRFs) to mine MRI brain images. As a proof-of-concept, we apply CRFs to the problem of brain tissue segmentation. Experimental results show robust and accurate performance on tissue segmentation comparable to other state-of-the-art segmentation methods. In addition, results show that our algorithm generalizes well across data sets and is less susceptible to outliers. Our method relies on minimal prior knowledge unlike atlas-based techniques, which assume images map to a normal template. Our results show that CRFs are a promising model for tissue segmentation, as well as other MRI data mining problems such as anatomical segmentation and disease diagnosis where atlas assumptions are unreliable in abnormal brain images.

Inverse Reinforcement Learning (IRL) is an approach for domain-reward discovery from demonstration, where an agent mines the reward function of a Markov decision process by observing an expert acting in the domain. In the standard setting, it is assumed that the expert acts (nearly) optimally, and a large number of trajectories, i.e., training examples are available for reward discovery (and consequently, learning domain behavior). These are not practical assumptions: trajectories are often noisy, and there can be a paucity of examples. Our novel approach incorporates advice-giving into the IRL framework to address these issues. Inspired by preference elicitation, a domain expert provides advice on states and actions (features) by stating preferences over them. We evaluate our approach on several domains and show that with small amounts of targeted preference advice, learning is possible from noisy demonstrations, and requires far fewer trajectories compared to simply learning from trajectories alone.

Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent example is Markov Logic Networks (MLNs). While MLNs are indeed highly expressive, this expressiveness comes at a cost. Learning MLNs is a hard problem and therefore has attracted much interest in the SRL community. Current methods for learning MLNs follow a two-step approach: first, perform a search through the space of possible clauses and then learn appropriate weights for these clauses. We propose to take a different approach, namely to learn both the weights and the structure of the MLN simultaneously. Our approach is based on functional gradient boosting where the problem of learning MLNs is turned into a series of relational functional approximation problems. We use two kinds of representations for the gradients: clause-based and tree-based. Our experimental evaluation on several benchmark data sets demonstrates that our new approach can learn MLNs as good or better than those found with state-of-the-art methods, but often in a fraction of the time.