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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

Method for Finding an Investment Strategy in the Case of a S...

引用

17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (纸本)9798350375718

关键词： Marketplaces

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Intelligent Assistant for Multivariant analysis 26

Intelligent Assistant for Multivariant Analysis

引用

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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ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Madhusudhanan, Kiran Behrens, Gunnar Stubbemann, Maximilian Schmidt-Thieme, Lars University Of Hildesheim Information Systems and Machine Learning Lab Vwfs Data Analytics Research Center Germany Volkswagen Financial Services Ag Data Analytics & Ai Engineering United States

ISBN: (纸本)9798350362480

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer durations. Our experiments further indicate that ProbSAINT is especially accurate in instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial. © 2024 IEEE.

关键词： Automotive industry

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Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation 36

Pitfalls of Epistemic Uncertainty Quantification through Los...

引用

36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium

ISBN: (纸本)9781713871088

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful way. © 2022 Neural information processing systems foundation. All rights reserved.

关键词： Probability distributions

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Towards Highly Efficient Anomaly Detection for Predictive Maintenance 23

Towards Highly Efficient Anomaly Detection for Predictive Ma...

引用

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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Investor Risk Profile Determination Model 16

Investor Risk Profile Determination Model

引用

16th International Conference Management of Large-Scale System Development, MLSD 2023

ISBN: (纸本)9798350337907

An assessment of the investor's risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market. © 2023 IEEE.

关键词： Convolution

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An Improved Finite-time analysis of Temporal Difference learning with Deep Neural Networks 41

An Improved Finite-time Analysis of Temporal Difference Lear...

引用

41st International Conference on machine learning, ICML 2024

作者： Ke, Zhifa Wen, Zaiwen Zhang, Junyu Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ϵ-1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ϵ-1) complexity under the Markovian sampling, as opposed to the best known Õ(ϵ-2) complexity in the existing literature. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification 23

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

引用

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

作者： Klüttermann, Simon Rutinowski, Jérôme Polachowski, Frederik Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel Paderborn University Paderborn Germany 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

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. © 2024 IEEE.

关键词： Contrastive learning

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Improving Generalization and Convergence by Enhancing Implicit Regularization 38

Improving Generalization and Convergence by Enhancing Implic...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Mingze Wang, Jinbo He, Haotian Wang, Zilin Huang, Guanhua Xiong, Feiyu Li, Zhiyu Weinan, E. Wu, Lei School of Mathematical Sciences Peking University China Center for Machine Learning Research Peking University China China AI for Science Institute China School of Data Science University of Science and Technology of China China ByteDance Research China

In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence. Specifically, IRE decouples the dynamics of flat and sharp directions, which boosts the sharpness reduction along flat directions while maintaining the training stability in sharp directions. We show that IRE can be practically incorporated with generic base optimizers without introducing significant computational overload. Experiments show that IRE consistently improves the generalization performance for image classification tasks across a variety of benchmark datasets (CIFAR-10/100, ImageNet) and models (ResNets and ViTs). Surprisingly, IRE also achieves a 2× speed-up compared to AdamW in the pre-training of Llama models (of sizes ranging from 60M to 229M) on datasets including Wikitext-103, Minipile, and Openwebtext. Moreover, we provide theoretical guarantees, showing that IRE can substantially accelerate the convergence towards flat minima in sharpness-aware minimization (SAM). © 2024 Neural information processing systems foundation. All rights reserved.

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Expected Probabilistic Hierarchies 38

Expected Probabilistic Hierarchies

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kollovieh, Marcel Charpentier, Bertrand Zügner, Daniel Günnemann, Stephan School of Computation Information and Technology Technical University of Munich Germany Munich Data Science Institute Germany Munich Center for Machine Learning Germany Pruna AI Germany Microsoft Research AI for Science United States

Hierarchical clustering has usually been addressed by discrete optimization using heuristics or continuous optimization of relaxed scores for hierarchies. In this work, we propose to optimize expected scores under a probabilistic model over hierarchies. (1) We show theoretically that the global optimal values of the expected Dasgupta cost and Tree-Sampling divergence (TSD), two unsupervised metrics for hierarchical clustering, are equal to the optimal values of their discrete counterparts contrary to some relaxed scores. (2) We propose Expected Probabilistic Hierarchies (EPH), a probabilistic model to learn hierarchies in data by optimizing expected scores. EPH uses differentiable hierarchy sampling enabling end-to-end gradient descent based optimization, and an unbiased subgraph sampling approach to scale to large datasets. (3) We evaluate EPH on synthetic and real-world datasets including vector and graph datasets. EPH outperforms all other approaches quantitatively and provides meaningful hierarchies in qualitative evaluations. © 2024 Neural information processing systems foundation. All rights reserved.

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