92 research outputs found

    Generalising weighted model counting

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    Given a formula in propositional or (finite-domain) first-order logic and some non-negative weights, weighted model counting (WMC) is a function problem that asks to compute the sum of the weights of the models of the formula. Originally used as a flexible way of performing probabilistic inference on graphical models, WMC has found many applications across artificial intelligence (AI), machine learning, and other domains. Areas of AI that rely on WMC include explainable AI, neural-symbolic AI, probabilistic programming, and statistical relational AI. WMC also has applications in bioinformatics, data mining, natural language processing, prognostics, and robotics. In this work, we are interested in revisiting the foundations of WMC and considering generalisations of some of the key definitions in the interest of conceptual clarity and practical efficiency. We begin by developing a measure-theoretic perspective on WMC, which suggests a new and more general way of defining the weights of an instance. This new representation can be as succinct as standard WMC but can also expand as needed to represent less-structured probability distributions. We demonstrate the performance benefits of the new format by developing a novel WMC encoding for Bayesian networks. We then show how existing WMC encodings for Bayesian networks can be transformed into this more general format and what conditions ensure that the transformation is correct (i.e., preserves the answer). Combining the strengths of the more flexible representation with the tricks used in existing encodings yields further efficiency improvements in Bayesian network probabilistic inference. Next, we turn our attention to the first-order setting. Here, we argue that the capabilities of practical model counting algorithms are severely limited by their inability to perform arbitrary recursive computations. To enable arbitrary recursion, we relax the restrictions that typically accompany domain recursion and generalise circuits (used to express a solution to a model counting problem) to graphs that are allowed to have cycles. These improvements enable us to find efficient solutions to counting fundamental structures such as injections and bijections that were previously unsolvable by any available algorithm. The second strand of this work is concerned with synthetic data generation. Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm’s superiority over another. However, benchmarks are often limited and fail to reveal differences among the algorithms. First, we show how random instances of probabilistic logic programs (that typically use WMC algorithms for inference) can be generated using constraint programming. We also introduce a new constraint to control the independence structure of the underlying probability distribution and provide a combinatorial argument for the correctness of the constraint model. This model allows us to, for the first time, experimentally investigate inference algorithms on more than just a handful of instances. Second, we introduce a random model for WMC instances with a parameter that influences primal treewidth—the parameter most commonly used to characterise the difficulty of an instance. We show that the easy-hard-easy pattern with respect to clause density is different for algorithms based on dynamic programming and algebraic decision diagrams than for all other solvers. We also demonstrate that all WMC algorithms scale exponentially with respect to primal treewidth, although at differing rates

    Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

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    [...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...

    Anomaly Detection with Variance Stabilized Density Estimation

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    Density estimation based anomaly detection schemes typically model anomalies as examples that reside in low-density regions. We propose a modified density estimation problem and demonstrate its effectiveness for anomaly detection. Specifically, we assume the density function of normal samples is uniform in some compact domain. This assumption implies the density function is more stable (with lower variance) around normal samples than anomalies. We first corroborate this assumption empirically using a wide range of real-world data. Then, we design a variance stabilized density estimation problem for maximizing the likelihood of the observed samples while minimizing the variance of the density around normal samples. We introduce an ensemble of autoregressive models to learn the variance stabilized distribution. Finally, we perform an extensive benchmark with 52 datasets demonstrating that our method leads to state-of-the-art results while alleviating the need for data-specific hyperparameter tuning.Comment: 12 pages, 6 figure

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

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    Bayesian network structure learning in the presence of latent variables

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    A causal Bayesian Network (BN) is a probabilistic graphical model that captures causal or conditional relationships between variables, and enables causal reasoning under uncertainty. Causal reasoning via graphical representation in turn enables interpretability and full transparency in decision-making, and this makes causal BNs suitable for modelling critical real-world problems that require explainability, such as in healthcare, environmental sciences, government policy and economics. Learning accurate causal structure from data represents a notoriously difficult task, and this difficulty increases with any imperfections present in the input data. For example, real data tend not to capture all relevant variables needed for causal representation, and these missing variables are referred to as hidden or latent variables. If some of the latent variables are latent confounders (i.e., missing common causes), they would confound the effect variables, thereby leading to spurious relationships in the learnt structure that could be misinterpreted as causal relationships. While the relevant literature includes structure learning algorithms that are capable of learning causal structure from data with latent variables, it is fair to say that accurate structural discovery from real data remains an open problem. This thesis studies structure learning algorithms that recover graphical structure from data, and primarily focuses on the problem of latent variables. It investigates new solutions, including structure learning algorithms that learn from both observational and interventional data, approaches for density estimation that can be used to recover the underlying distribution of possible latent confounders, and techniques for hyperparameter optimisation of structure learning algorithms. The thesis explores this set of new approaches by applying them to a range of synthetic and real datasets of varying size, dimensionality, and data noise, and concludes by highlighting open problems and directions for future research

    Energy Data Analytics for Smart Meter Data

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    The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Intelligent Sensors for Human Motion Analysis

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    The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems
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