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arXiv 2024年

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

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ n×d, proportional to their p norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to Ε relative error. When combined with preconditioning techniques, our algorithm extends to p leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective offline subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + Ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/Ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for p and logistic regression. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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Online Query-Based data Pricing with Time-Discounting Valuations

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

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International Conference on data Engineering

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

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

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 $O(\frac{T}{k})$ , where $K$ and $T$ are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound $O(K^{3}\sqrt{T\ln T}+K\exp\{4\sqrt{\ln T}\})$ . 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.

关键词： Upper bound Heuristic algorithms Pricing machine learning Market research data engineering Task analysis

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Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation 24

Optimal bounds for ℓp sensitivity sampling via ℓ2 augmenta...

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Proceedings of the 41st International Conference on machine Learning

作者： Alexander Munteanu Simon Omlor Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany and 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 $\tilde O(\varepsilon^{-2}\mathfrak Sd)$ bound for the important problem of ℓp subspace embeddings to $\tilde O(\varepsilon^{-2}\mathfrak S^{2/p})$ for p ∈ [1, 2]. Their result was subsumed by an earlier $\tilde O(\varepsilon^{-2}\mathfrak Sd^{1-p/2})$ bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain ℓp sensitivities. We observe that by augmenting the ℓp sensitivities by ℓ2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear $\tilde O(\varepsilon^{-2}(\mathfrak S+d)) = \tilde O(\varepsilon^{-2}d)$ 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.

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Turnstile ℓp leverage score sampling with applications 24

Turnstile ℓp leverage score sampling with applications

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Proceedings of the 41st International Conference on machine Learning

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ ℝn×d, proportional to their ℓp norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to e relative error. When combined with preconditioning techniques, our algorithm extends to ℓp leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective off-line subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for ℓp and logistic regression.

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On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification

On the Effectiveness of Heterogeneous Ensemble Methods for R...

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International Conference on machine Learning and Applications (ICMLA)

作者： Simon Klüttermann Jérôme Rutinowski Frederik Polachowski Anh Nguyen Britta Grimme Moritz Roidl Emmanuel Müller TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Paderborn University Paderborn Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods.

关键词： Training Accuracy Neural networks Metals Feature extraction Silicon Ensemble learning Security Reliability Pallets

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Marine Predators Algorithm for Energy Scheduling Problem Using Renewable Energy

Marine Predators Algorithm for Energy Scheduling Problem Usi...

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Cyber Resilience (ICCR), International Conference on

作者： Sharif Naser Makhadmeh Ammar Kamal Abasi Mohammed Azmi Al-Betar Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Energy Scheduling Problem (ESP) involves scheduling smart home appliances based on electricity pricing schemes. This entails adjusting the timing of operations for these appliances across different periods. The primary aim of this scheduling approach is to reduce cost and the Peak-to-Average Ratio (PAR), while enhancing user comfort. This study employs the Marine Predators Algorithm (MPA) to address ESP and derive an optimal schedule for smart home appliances. Additionally, a renewable energy source (RES) on the basis of a PV system is introduced to further optimize appliance schedules by providing a smart home with energy, particularly during peak periods. The obtained outcomes demonstrate the effectiveness of the introduced RES in reducing costs of electricity and enhancing user satisfaction. Furthermore, the performance of MPA is compared with that of the wind-driven optimization algorithm, with MPA proving superior in achieving ESP objectives.

关键词： Home appliances Optimization Pricing Heuristic algorithms Optimal scheduling Electricity Dynamic scheduling

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Universality of kernel random matrices and kernel regression in the quadratic regime

arXiv

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arXiv 2024年

作者： Pandit, Parthe Wang, Zhichao Zhu, Yizhe Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Mathematics University of California San Diego United States Department of Mathematics University of Southern California United States

Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much of the focus has been on studying the proportional asymptotic regime, n ≍ d, where n is the number of training samples and d is the dimension of the dataset. In this regime, under certain conditions on the data distribution, the kernel random matrix involved in KRR exhibits behavior akin to that of a linear kernel. In this work, we extend the study of kernel regression to the quadratic asymptotic regime, where n ≍ d2. In this regime, we demonstrate that a broad class of inner-product kernels exhibit behavior similar to a quadratic kernel. Specifically, we establish an operator norm approximation bound for the difference between the original kernel random matrix and a quadratic kernel random matrix with additional correction terms compared to the Taylor expansion of the kernel functions. The approximation works for general data distributions under a Gaussian-moment-matching assumption with a covariance structure. This new approximation is utilized to obtain a limiting spectral distribution of the original kernel matrix and characterize the precise asymptotic training and generalization errors for KRR in the quadratic regime when n/d2 converges to a non-zero constant. The generalization errors are obtained for both deterministic and random teacher models. Our proof techniques combine moment methods, Wick’s formula, orthogonal polynomials, and resolvent analysis of random matrices with correlated entries. © 2024, CC BY.

关键词： Deep learning

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Personalised Speech-Based PTSD Prediction Using Weighted-Instance Learning 46

Personalised Speech-Based PTSD Prediction Using Weighted-Ins...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Kathan, Alexander Amiriparian, Shahin Triantafyllopoulos, Andreas Gebhard, Alexander Milkus, Sabrina Hohmann, Jonas Muderlak, Pauline Schottdorf, Jürgen Musil, Richard Schuller, Björn W. University of Augsburg Eihw - Embedded Intelligence for Healthcare and Wellbeing Germany Mri Technical Univsersity of Munich Chi - Health Informatics Germany Mcml - Munich Center for Machine Learning Germany University Hospital Lmu Munich Department of Psychiatry and Psychotherapy Germany Zentrumspraxis Friedberg Germany Imperial College London Glam - Group on Language Audio & Music United Kingdom Mdsi - Munich Data Science Institute Germany

ISBN: (纸本)9798350371499

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples' daily life and can affect their mental, physical, or social wellbeing, which is why a timely and professional treatment is required. In this paper, we propose a personalised speech-based PTSD prediction approach using a newly collected dataset which consists of 15 participants, including speech recordings from people with PTSD and healthy controls. In addition, the dataset includes data before and after a clinical intervention so that the prediction can be analysed at different points in time. In our experiments, we demonstrate the superiority of the personalised approach, achieving a best area under the ROC curve (AUC) of 82 % and a best relative improvement of 7 % points compared to the non-personalised model. © 2024 IEEE.

关键词： Prediction models

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An Experimental Survey of Missing data Imputation Algorithms (Extended Abstract)

An Experimental Survey of Missing Data Imputation Algorithms...

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International Conference on data Engineering

作者： Xiaoye Miao Yangyang Wu Lu Chen Yunjun Gao Jianwei Yin Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Software College Zhejiang University Ningbo China College of Computer Science Zhejiang University Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle missing data problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation.

关键词： Surveys Systematics Benchmark testing Prediction algorithms data engineering Imputation data models

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An empirical examination of balancing strategy for counterfactual estimation on time series 24

An empirical examination of balancing strategy for counterfa...

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Proceedings of the 41st International Conference on machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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