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The Journal of machine learning Research 2021年第1期22卷 11184-11254页

作者： Alden Green Sivaraman Balakrishnan Ryan J. Tibshirani Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University Pittsburgh PA

We study the Personalized PageRank (PPR) algorithm, a local spectral method for clustering, which extracts clusters using locally-biased random walks around a given seed node. In contrast to previous work, we adopt a classical statistical learning setup, where we obtain samples from an unknown nonparametric distribution, and aim to identify sufficiently salient clusters. We introduce a trio of population-level functionals--the normalized cut, conductance, and local spread, analogous to graph-based functionals of the same name--and prove that PPR, run on a neighborhood graph, recovers clusters with small population normalized cut and large conductance and local spread. We apply our general theory to establish that PPR identifies connected regions of high density (density clusters) that satisfy a set of natural geometric conditions. We also show a converse result, that PPR can fail to recover geometrically poorly-conditioned density clusters, even asymptotically. Finally, we provide empirical support for our theory.

关键词： graphs spectral clustering personalized PageRank density clustering unsupervised learning

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Pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

Pattern Discovery in an EEG Database of Depression Patients:...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement science, Slovak Academy o.

关键词： data mining

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A Permutation-Free Kernel Independence Test

arXiv

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arXiv 2022年

作者： Shekhar, Shubhanshu Kim, Ilmun Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Yonsei University Korea Republic of Department of Applied Statistics Yonsei University Korea Republic of

In nonparametric independence testing, we observe i.i.d. data {(Xi, Yi)}n/i=1, where X ∈ X, Y ∈ Y lie in any general spaces, and we wish to test the null that X is independent of Y. Modern test statistics such as the kernel Hilbert-Schmidt Independence Criterion (HSIC) and Distance Covariance (dCov) have intractable null distributions due to the degeneracy of the underlying U-statistics. Thus, in practice, one often resorts to using permutation testing, which provides a nonasymptotic guarantee at the expense of recalculating the quadratic-time statistics (say) a few hundred times. This paper provides a simple but nontrivial modification of HSIC and dCov (called xHSIC and xdCov, pronounced "cross" HSIC/dCov) so that they have a limiting Gaussian distribution under the null, and thus do not require permutations. This requires building on the newly developed theory of cross U-statistics by Kim and Ramdas [2020], and in particular developing several nontrivial extensions of the theory in Shekhar et al. [2022], which developed an analogous permutation-free kernel two-sample test. We show that our new tests, like the originals, are consistent against fixed alternatives, and minimax rate optimal against smooth local alternatives. Numerical simulations demonstrate that compared to the full dCov or HSIC, our variants have the same power up to a √2 factor, giving practitioners a new option for large problems or data-analysis pipelines where computation, not sample size, could be the bottleneck. © 2022, CC BY.

关键词： Computation theory

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Extrapolated cross-validation for randomized ensembles

arXiv

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arXiv 2023年

作者： Du, Jin-Hong Patil, Pratik Roeder, Kathryn Kuchibhotla, Arun Kumar Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Department of Statistics University of California BerkeleyCA94720 United States

Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This paper introduces a cross-validation method, ECV (Extrapolated Cross-Validation), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields δ-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and K-fold cross-validation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Generalized equivalences between subsampling and ridge regularization

arXiv

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arXiv 2023年

作者： Patil, Pratik Du, Jin-Hong Department of Statistics University of California BerkeleyCA94720 United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

We establish precise structural and risk equivalences between subsampling and ridge regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic functionals of subsample ridge estimators, when fitted with different ridge regularization levels λ and subsample aspect ratios ψ, are asymptotically equivalent along specific paths in the (λ, ψ)-plane (where ψ is the ratio of the feature dimension to the subsample size). Our results only require bounded moment assumptions on feature and response distributions and allow for arbitrary joint distributions. Furthermore, we provide a data-dependent method to determine the equivalent paths of (λ, ψ). An indirect implication of our equivalences is that optimally tuned ridge regression exhibits a monotonic prediction risk in the data aspect ratio. This resolves a recent open problem raised by Nakkiran et al. [1] for general data distributions under proportional asymptotics, assuming a mild regularity condition that maintains regression hardness through linearized signal-to-noise ratios. Copyright © 2023, The Authors. All rights reserved.

关键词： Aspect ratio

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Smart Finance: An overview of Artificial Intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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Liver Disease Prediction using machine learning Classification Techniques 11

Liver Disease Prediction using Machine learning Classificati...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Gupta, Ketan Jiwani, Nasmin Afreen, Neda Divyarani, D. University of the Cumberlands United States Jamia Millia Islamia Department of Computer Engineering New Delhi India Pesit University Electronic City Data Science and Machine Learning Bangaluru India

ISBN: (纸本)9781665480383

machine learning is a process which is used to discover patterns in huge data/ large data set to enable decision, thereby allowing machines to go through a learning process (i.e. supervised, unsupervised and semi-supervised or reinforced). The data set used in this paper is Liver Patient taken from UCI Repository (i.e. Supervised learning). There is a plenty of data on patients undergoing medical examination at hospitals and these data has been extracted on liver patients whose information can be further used for future improvement of their conditions. In other words, historical and classified input of patients and output data is fed into various algorithms or classifiers for predicting the future data of patients. The algorithms used here for predicting liver patients are Logistic regression, Decision Tree, Random Forest, KNNeighbor, Gradient Boosting, Extreme Gradient Boosting, LightGB. Based on the analysis and result calculations, it was found that these algorithm has obtained good accuracy after feature selection. © 2022 IEEE.

关键词： Decision trees

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Revealing excited states of rotational Bose-Einstein condensates

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The Innovation 2024年第1期5卷 41-48页

作者： Jianyuan Yin Zhen Huang Yongyong Cai Qiang Du Lei Zhang School of Mathematical Sciences Laboratory of Mathematics and Applied MathematicsPeking UniversityBeijing 100871China Department of Mathematics National University of SingaporeSingapore 119076Singapore Department of Mathematics University of CaliforniaBerkeleyBerkeleyCA 94720USA School of Mathematical Sciences Beijing Normal UniversityBeijing 100875China Department of Applied Physics and Applied Mathematics and Data Science Institute Columbia UniversityNew YorkNY 10027USA Beijing International Center for Mathematical Research Center for Quantitative BiologyCenter for Machine Learning ResearchPeking UniversityBeijing 100871China

Rotational Bose-Einstein condensates can exhibit quantized vortices as topological *** this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the stationary points of the Gross-Pitaevskii energy *** excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics *** excitation mechanisms are identified:vortex addition,rearrangement,merging,and *** demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states.

关键词： method rotational excited

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Optimal Kernel Choice for Score Function-based Causal Discovery 41

Optimal Kernel Choice for Score Function-based Causal Discov...

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41st International Conference on machine learning, ICML 2024

作者： Wang, Wenjie Huang, Biwei Liu, Feng You, Xinge Liu, Tongliang Zhang, Kun Gong, Mingming School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of California San Diego United States School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Philosophy Carnegie Mellon University United States

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods. Copyright 2024 by the author(s)

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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