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The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series data and Sequential Decision Making

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IEEE Transactions on Pattern Analysis and machine Intelligence 2025年第7期47卷 5901-5917页

作者： Shujian Yu Hongming Li Sigurd Løkse Robert Jenssen José C. Príncipe Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway Quantitative Data Analytics Group Vrije Universiteit Amsterdam Amsterdam The Netherlands Department of Electrical and Computer Engineering University of Florida Gainesville FL USA Drones and Autonomous Systems Group NORCE Norwegian Research Centre Tromsø Norway Pioneer AI Centre Copenhagen University København Denmark Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be elegantly estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional Kullback-Leibler divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making.

关键词： Covariance matrices Kernel machine learning Time series analysis Vectors Symmetric matrices Q measurement Matrix decomposition Estimation Decision making

Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization

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arXiv 2024年

作者： Dietrich, Konstantin Prager, Raphael Patrick Doerr, Carola Trautmann, Heike Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Germany Sorbonne Université CNRS LIP6 Paris France Machine Learning and Optimisation Paderborn University Germany Data Management and Biometrics Group University of Twente Netherlands

Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated algorithm selection and configuration. Until recently, however, ELA was limited to search spaces with either continuous or discrete variables, neglecting problems with mixed variable types. This gap was addressed in a recent study that uses an approach based on target-encoding to compute exploratory landscape features for mixed-variable problems. In this work, we investigate an alternative encoding scheme based on SHAP values. While these features do not lead to better results in the algorithm selection setting considered in previous work, the two different encoding mechanisms exhibit complementary performance. Combining both feature sets into a hybrid approach outperforms each encoding mechanism individually. Finally, we experiment with two different ways of meta-selecting between the two feature sets. Both approaches are capable of taking advantage of the performance complementarity of the models trained on target-encoded and SHAP-encoded feature sets, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

Carbon Stock Estimation at Scale from Aerial and Satellite Imagery

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Carbon Stock Estimation at Scale from Aerial and Satellite I...

Artificial Intelligence (CAI), IEEE Conference on

作者： Alex To Hoang Quoc Viet Pham Quang H. Nguyen Joseph G. Davis Barry O’Sullivan Shan L Pan Hoang D. Nguyen University of Sydney Sydney Australia University College Cork Cork Ireland Reliable Machine Learning Group Hanoi Vietnam Insight Centre for Data Analytics University College Cork Ireland Institute of Climate Risk and Response University of New South Wales Australia

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

In the ongoing efforts to mitigate climate change effect, the capability to reliably estimate forest carbon stock on a global scale is vital to support sustainable development. This entails the investigation of tree coverage from diverse forest ecosystems worldwide, necessitating a substantial volume of high-resolution images. This paper integrates a variety of remote sensing data sources, from aerial to satellite imagery, for the training and development of our AI system. Given the heterogeneous nature of these data sources, we develop a standardization method to ensure consistent image size and resolution between source platforms. Our harmonized dataset includes 86,088 training images and 21,768 validation images, each with a high resolution of 1.194 m 2 per pixel. We introduce a novel technique for tree semantic segmentation which offers a more effective alternative to traditional individual tree crown delineation for large-scale tree coverage estimation. To assess the adaptability of our AI models, we conducted experiments on a hand-annotated satellite image test set and achieved a High Vegetation IoU score of 45.73%. Building on these findings, we present an interactive web-based Geographic Information System for navigating high vegetation segmented satellite images and estimating carbon stock on a global scale.

关键词： Remote sensing Satellite images Semantic segmentation Climate change Forestry Carbon Globalization Vegetation mapping Soft sensors Image resolution Artificial intelligence Sustainable development Estimation

Bayesian Inference of Transition Matrices from Incomplete Graph data with a Topological Prior

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arXiv 2022年

作者： Perri, Vincenzo Petrović, Luka V. Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

Many network analysis and graph learning techniques are based on discrete- or continuous-time models of random walks. To apply these methods, it is necessary to infer transition matrices that formalize the underlying stochastic process in an observed graph. For weighted graphs, where weighted edges capture observations of repeated interactions between nodes, it is common to estimate the entries of such transition matrices based on the (relative) weights of edges. However in real-world settings we are often confronted with incomplete data, which turns the construction of the transition matrix based on a weighted graph into an inference problem. Moreover, we often have access to additional information, which capture topological constraints of the system, i.e. which edges in a weighted graph are (theoretically) possible and which are not. Examples include transportation networks, where we may have access to a small sample of passenger trajectories as well as the physical topology of connections, or a limited set of observed social interactions with additional information on the underlying social structure. Combining these two different sources of information to reliably infer transition matrices from incomplete data on repeated interactions is an important open challenge, with severe implications for the reliability of downstream network analysis tasks. Addressing this issue, we show that including knowledge on such topological constraints can considerably improve the inference of transition matrices, especially in situations where we only have a small number of observed interactions. To this end, we derive an analytically tractable Bayesian method that uses repeated interactions and a topological prior to perform data-efficient inference of transition matrices. We compare our approach against commonly used frequentist and Bayesian approaches both in synthetic data and in five real-world datasets, and we find that our method recovers the transition probabilities with h

关键词： Stochastic systems

A Network Perspective on the Influence of Code Review Bots on the Structure of Developer Collaborations

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arXiv 2023年

作者： Röseler, Leonore Scholtes, Ingo Gote, Christoph Data Analytics Group University of Zurich Zurich Switzerland Social Computing Group University of Zurich Zurich Switzerland Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany Systems Design ETH Zurich Zurich Switzerland

Background: Despite a growing body of literature on the impact of software bots on open source software development teams, their effects on team communication, coordination, and collaboration practices are not well understood. Bots can have negative consequences, such as producing information overload or reducing interactions between developers. Objective: The objective of this study is to investigate the effect of specific GitHub Actions on the collaboration networks of Open Source Software teams, using a network-analytic approach. The study will focus on Code Review bots, one of the most frequently implemented types of bots. Method: Fine-grained, time-stamped data of co-editing networks, developer-file contribution networks, as well as workflow runs and git commit logs will be obtained from a large sample of GitHub repositories. This will allow us to study how bots affect the collaboration networks of developers over time. By using a more representative sample of GitHub repositories than previous studies, which includes projects whose sizes span the whole range of Open Source communities, this study will provide generalizable results and updated findings on the general usage and distribution of GitHub Actions. With this study, we aim to contribute to advancing our knowledge of human-bot interaction and the effects of support tools on software engineering teams. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

machine learning algorithms to classify Fitzpatrick skin types during tissue oxygenation mapping

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Machine learning algorithms to classify Fitzpatrick skin typ...

Clinical and Translational Biophotonics, Translational 2022

作者： Kaile, Kacie Sobhan, Masrur Mondal, Ananda Godavarty, Anuradha Department of Biomedical Engineering Optical Imaging Laboratory Florida International University 10555 West Flagler St MiamiFL33174 United States Machine Learning and Data Analytics Group Knight Foundation School of Computing and Information Sciences Florida International University 11200 SW 8th St MiamiFL33199 United States

A machine learning approach is implemented to label different skin tones towards melanin correction of tissue oxygenation maps, when using a smartphone based near-infrared (NIR) imaging device. © 2022 Optica Publ... 详细信息

ISBN: (纸本)9781557528209

关键词： Tissue

Design and Implementation of a data Governance Framework and Platform: A Case Study of a National Research Organization of Thailand

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Design and Implementation of a Data Governance Framework and...

International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sapa Chanyachatchawan Krich Nasingkun Patipat Tumsangthong Porntiwa Chata Marut Buranarach Monsak Socharoentum Leveraging Technology Solutions Section National Electronics and Computer Technology Center Bangkok Thailand Strategic Analytics Networks with Machine Learning and AI Research National Electronics and Computer Technology Center Bangkok Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Bangkok Thailand Digital Government Development Agency Bangkok Thailand

In the current era of extensive data usage across industries, data collection, preservation, utilization, and organization has become more challenging and nuanced because it is necessary to consider critical concerns such as data security, privacy, and legal issues, apart from efficiency issues. As a result, Thai government initiated the idea and effort to implement data governance throughout the government agency. This paper showcases the implementation of data governance in a governmental research organization with highly diverse structured and unstructured data. The implementation follows international standards and the guidelines of the Digital Government Development Agency (DGA). The executives set up the working body, including the data Governance Council and data Stewards, responsible for setting up and deploying policies and regulations. Creating awareness and the necessary infrastructure are the main focuses in the first-year phase. The metadata was designed to extend DGA's version and match the organization's unique requirements. A data catalog platform was developed accordingly. We organized activities to boost employee awareness and participation, including advertising and data catalog platform training. By the end of the first year of implementation, every organization unit had registered at least one data record into the data catalog.

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Predicting Influential Higher-Order Patterns in Temporal Network data

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Predicting Influential Higher-Order Patterns in Temporal Net...

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Christoph Gote Vincenzo Perri Ingo Scholtes Data Analytics Group University of Zurich Zurich Switzerland Chair of Systems Design ETH Zurich Switzerland Chair of Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany

Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly influence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. On the one hand, to avoid overfitting, such models should only consider those higher-order patterns for which the data provide sufficient statistical evidence. On the other hand, we hypothesise that network models, which capture only direct interactions, underfit higher-order patterns present in data. Consequently, both approaches are likely to misidentify influential nodes in complex networks. We contribute to this issue by proposing five centrality measures based on MOGen, a multi-order generative model that accounts for all indirect influences up to a maximum distance but disregards influences at higher distances. We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data. Our results show strong evidence supporting our hypothesis. MOGen consistently outperforms both the network model and path-based prediction. We further show that the performance difference between MOGen and the path-based approach disappears if we have sufficient observations, confirming that the error is due to overfitting.

关键词： Social networking (online) Computational modeling Complex networks Predictive models Length measurement data models Topology

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series data and Sequential Decision Making

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arXiv 2023年

作者： Yu, Shujian Li, Hongming Løkse, Sigurd Jenssen, Robert Príncipe, José C. The Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway The Quantitative Data Analytics Group Vrije Universiteit Amsterdam Netherlands The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States The Drones and Autonomous Systems group at NORCE Norwegian Research Centre Tromsø Norway The Pioneer AI Centre Copenhagen University The Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional KL divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning