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26th International Conference of the Catalan Association for Artificial intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks 38

The Map Equation Goes Neural: Mapping Network Flows with Gra...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Blöcker, Christopher Tan, Chester 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

Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised with custom-tailored search algorithms, but often without leveraging recent advances in deep learning. Recently, first works have started incorporating such objectives into loss functions for deep graph clustering and pooling. We consider the map equation, a popular information-theoretic objective function for unsupervised community detection, and express it in differentiable tensor form for optimisation through gradient descent. Our formulation turns the map equation compatible with any neural network architecture, enables end-to-end learning, incorporates node features, and chooses the optimal number of clusters automatically, all without requiring explicit regularisation. Applied to unsupervised graph clustering tasks, we achieve competitive performance against state-of-the-art deep graph clustering baselines in synthetic and real-world datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Feature maps for the Laplacian kernel and its generalizations

arXiv

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arXiv 2025年

作者： Ahir, Sudhendu Pandit, Parthe Center for Machine Intelligence and Data Science IIT Bombay India

Recent applications of kernel methods in machine learning have seen a renewed interest in the Laplacian kernel, due to its stability to the bandwidth hyperparameter in comparison to the Gaussian kernel, as well as its expressivity being equivalent to that of the neural tangent kernel of deep fully connected networks. However, unlike the Gaussian kernel, the Laplacian kernel is not separable. This poses challenges for techniques to approximate it, especially via the random Fourier features (RFF) methodology and its variants. In this work, we provide random features for the Laplacian kernel and its two generalizations: Matérn kernel and the Exponential power kernel. We provide efficiently implementable schemes to sample weight matrices so that random features approximate these kernels. These weight matrices have a weakly coupled heavy-tailed randomness. Via numerical experiments on real datasets we demonstrate the efficacy of these random feature maps. © 2025, CC BY-SA.

关键词： Laplace transforms

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Towards Highly Efficient Anomaly Detection for Predictive Maintenance 23

Towards Highly Efficient Anomaly Detection for Predictive Ma...

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23rd IEEE International Conference on machine Learning and Applications, ICMLA 2024

作者： Klüttermann, Simon Peka, Vanlal Doebler, Philipp Müller, Emmanuel Tu Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (纸本)9798350374889

This paper introduces SEAN, a novel anomaly detection algorithm designed for real-time applications in predictive maintenance. SEAN leverages an ensemble-based approach to deliver competitive performance while drastically reducing computational costs. In our comprehensive evaluation across 121 datasets, SEAN consistently outperforms comparable shallow anomaly detection algorithms. Our comparisons reveal that SEAN operates over 20,000 times faster than a similar state-of-the-art deep learning alternative, with negligible sacrifice in detection accuracy. We further demonstrate SEAN's versatility through an ablation study, highlighting how its hyperparameters can be tuned to balance runtime and performance effectively. Additionally, we present a practical C++ export tool that enables the deployment of SEAN on resource-constrained devices, meeting the stringent requirements of on-device predictive maintenance tasks. Our findings underscore SEAN as a powerful and efficient solution for anomaly detection in real-world engineering applications. © 2024 IEEE.

关键词： Predictive maintenance

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Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling 23

Reinforcement Learning as an Improvement Heuristic for Real-...

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23rd IEEE International Conference on machine Learning and Applications, ICMLA 2024

作者： Müller, Arthur Vollenkemper, Lukas Fraunhofer IOSB-INA Department of Machine Intelligence Lemgo32657 Germany Center for Applied Data Science Bielefeld University of Applied Sciences and Arts Gütersloh33330 Germany

ISBN: (纸本)9798350374889

The integration of Reinforcement Learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL's ability to learn from the data generated during the search process. One promising approach is to train an RL agent as an improvement heuristic, starting with a suboptimal solution that is iteratively improved by applying small changes. We apply this approach to a real-world multiobjective production scheduling problem. Our approach utilizes a network architecture that includes Transformer encoding to learn the relationships between jobs. Afterwards, a probability matrix is generated from which pairs of jobs are sampled and then swapped to improve the solution. We benchmarked our approach against other heuristics using real data from our industry partner, demonstrating its superior performance. © 2024 IEEE.

关键词： Reinforcement learning

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Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling 29

Smaller Batches, Bigger Gains? Investigating the Impact of B...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Müller, Arthur Grumbach, Felix Sabatelli, Matthia Fraunhofer IOSB-INA Department of Machine Intelligence Lemgo32657 Germany Center for Applied Data Science Hochschule Bielefeld Gütersloh33330 Germany University of Groningen Department of Artificial Intelligence Groningen9712 CP Netherlands

ISBN: (纸本)9798350361230

Production scheduling is an essential task in manufacturing, with Reinforcement Learning (RL) emerging as a key solution. In a previous work, RL was utilized to solve an extended permutation flow shop scheduling problem (PFSSP) for a real-world production line with two stages, linked by a central buffer. The RL agent was trained to sequence equallysized product batches to minimize setup efforts and idle times. However, the substantial impact caused by varying the size of these product batches has not yet been explored. In this follow-up study, we investigate the effects of varying batch sizes, exploring both the quality of solutions and the training dynamics of the RL agent. The results demonstrate that it is possible to methodically identify reasonable boundaries for the batch size. These boundaries are determined on one side by the increasing sample complexity associated with smaller batch sizes, and on the other side by the decreasing flexibility of the agent when dealing with larger batch sizes. This provides the practitioner the ability to make an informed decision regarding the selection of an appropriate batch size. Moreover, we introduce and investigate two new curriculum learning strategies to enable the training with small batch sizes. The findings of this work offer the potential for application in several industrial use cases with comparable scheduling problems. © 2024 IEEE.

关键词： Reinforcement learning

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GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs 38

GraphMorph: Tubular Structure Extraction by Morphing Predict...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Zhao Zhao, Ziwei Wang, Dong Wang, Liwei Center for Data Science Peking University China Yizhun Medical AI Co. Ltd. China State Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Machine Learning Research Peking University China Guangdong Guangzhou China

Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon. © 2024 Neural information processing systems foundation. All rights reserved.

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Online Query-Based data Pricing with Time-Discounting Valuations 40

Online Query-Based Data Pricing with Time-Discounting Valuat...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Fu, Yicheng Miao, Xiaoye Peng, Huanhuan Na, Chongning Deng, Shuiguang Yin, Jianwei Zhejiang University Center for Data Science Hangzhou China Zhejiang University The State Key Lab of Brain-Machine Intelligence Hangzhou China PinTech Research Center Zhejiang Lab Hangzhou China College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350317152

Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error O(Tk), where K and T are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound O(K3√TlnT+Kexp{4√ln T}). Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods. © 2024 IEEE.

关键词： Costs

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Optimal bounds for p sensitivity sampling via 2 augmentation 41

Optimal bounds for p sensitivity sampling via 2 augmentation

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41st International Conference on machine Learning, ICML 2024

作者： Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1 ± Ε) guarantees on the quality of approximation. The recent work of Woodruff & Yasuda (2023c) improved substantially over the general Õ(Ε−2Sd) bound for the important problem of p subspace embeddings to Õ(Ε−2S2/p) for p ∈ [1, 2]. Their result was subsumed by an earlier Õ(Ε−2Sd1−p/2) bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain p sensitivities. We observe that by augmenting the p sensitivities by 2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear Õ(Ε−2(S+d)) = Õ(Ε−2d) sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff & Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen & Peng, 2015). As an application of our main result, we also obtain an Õ(Ε−2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ(Ε−2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling. Copyright 2024 by the author(s)

关键词： Importance sampling

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