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arXiv 2023年

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

It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture so-called aleatoric uncertainty of predictions, various machine learning methods have recently been developed with the goal to let the learner also represent its epistemic uncertainty, i.e., the uncertainty caused by a lack of knowledge and data. An emerging branch of the literature proposes the use of a second-order learner that provides predictions in terms of distributions on probability distributions. However, recent work has revealed serious theoretical shortcomings for second-order predictors based on loss minimisation. In this paper, we generalise these findings and prove a more fundamental result: There seems to be no loss function that provides an incentive for a second-order learner to faithfully represent its epistemic uncertainty in the same manner as proper scoring rules do for standard (first-order) learners. As a main mathematical tool to prove this result, we introduce the generalised notion of second-order scoring *** Codes 68T37 (Primary) 68T30 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Track C1: Safety Verification of Deep Neural Networks (DNNs) 1st

Track C1: Safety Verification of Deep Neural Networks (DNNs...

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1st International Conference on Bridging the Gap between AI and Reality, AISoLA 2023

作者： Neider, Daniel Johnson, Taylor T. Chair for Verification and Formal Guarantees of Machine Learning TU Dortmund University Dortmund Germany Research Center for Trustworthy Data Science and Security Dortmund Germany Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031460012

Formal verification of neural networks and broader machine learning models is an emerging field that has gained significant attention due to the growing use and impact of these data-driven methods. This track explores techniques for formally verifying neural networks and other machine learning models across various application domains. It includes papers and presentations discussing new methodologies, software frameworks, technical approaches, and case studies. Benchmarks play a crucial role in evaluating the effectiveness and scalability of these methods. Currently, available benchmarks mainly focus on computer vision problems, such as local robustness to adversarial perturbations of image classifiers. To address this limitation, this track compiles and publishes benchmarks comprising machine learning models and their specifications across domains such as computer vision, finance, security, and others. These benchmarks will help assess the suitability and applicability of formal verification methods in diverse domains. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Formal Methods Formal Verification Neural Networks Safety of Autonomy

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Channel Dependence, Limited Lookback Windows, and the Simplicity of datasets: How Biased is Time Series Forecasting?

arXiv

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arXiv 2025年

作者： Abdelmalak, Ibram Madhusudhanan, Kiran Choi, Jungmin Stubbemann, Maximilian Schmidt-Thieme, Lars Information Science and Machine Learning Lab VWFS Data Analytics Research Center University of Hildesheim Niedersachsen Hildesheim Germany

Time-series forecasting research has converged to a small set of datasets and a standardized collection of evaluation scenarios. Such a standardization is to a specific extent needed for comparable research. However, the underlying assumption is, that the considered setting is a representative for the problem as a whole. In this paper, we challenge this assumption and show that the current scenario gives a strongly biased perspective on the state of time-series forecasting research. To be more detailed, we show that the current evaluation scenario is heavily biased by the simplicity of the current datasets. We furthermore emphasize, that when the lookback-window is properly tuned, current models usually do not need any information flow across channels. However, when using more complex benchmark data, the situation changes: Here, modeling channel-interactions in a sophisticated manner indeed enhances performances. Furthermore, in this complex evaluation scenario, Crossformer, a method regularly neglected as an important baseline, is the SOTA method for time series forecasting. Based on this, we present the Fast Channel-dependent Transformer (FaCT), a simplified version of Crossformer which closes the runtime gap between Crossformer and TimeMixer, leading to an efficient model for complex forecasting datasets. © 2025, CC BY.

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TempXAI: Explainable AI for Time Series and data Streams 2024

TempXAI: Explainable AI for Time Series and Data Streams 202...

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2024 Workshop on Explainable AI for Time Series and data Streams, TempXAI 2024

作者： Abdallah, Zahraa Fumagalli, Fabian Hammer, Barbara Hüllermeier, Eyke Jakobs, Matthias Müller, Emmanuel Muschalik, Maximilian Papapetrou, Panagiotis Saadallah, Amal Tzagkarakis, George University of Bristol United Kingdom Bielefeld University Germany LMU Munich Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany Research Center Trustworthy Data Science and Security Germany Stockholm University Sweden FORTH-ICS Greece

This document is the preface of the proceedings of the TempXAI Workshop Tutorial on Explainable AI for Time Series and data Streams, held on September 9th, 2024, in Vilnius, Lithuania. We received 5 submissions for peer-review, out of which we accepted 4 papers for this volume. In addition, we publish two extended abstracts of the tutorial that we give as a part of the workshop program. © 2024 CEUR-WS. All rights reserved.

关键词： data streams

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Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting

arXiv

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arXiv 2024年

作者： Madhusudhanan, Kiran Jawed, Shayan Schmidt-Thieme, Lars Information Systems and Machine Learning Lab VWFS Data Analytics Research Center University of Hildesheim Hildesheim Germany

Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research primarily focuses on examining the impact of specific hyperparameters related to time series, such as context length and validation strategy, on the performance of the state-of-the-art MLP model in time series forecasting. We have conducted a comprehensive series of experiments involving 4800 configurations per dataset across 20 time series forecasting datasets, and our findings demonstrate the importance of tuning these parameters. Furthermore, in this work, we introduce the largest metadataset for time series forecasting to date, named TSBench, comprising 97200 evaluations, which is a twentyfold increase compared to previous works in the field. Finally, we demonstrate the utility of the created metadataset on multi-fidelity hyperparameter optimization tasks. © 2024, CC BY.

关键词： Forecasting

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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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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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Deep learning approaches in flow visualization

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Advances in Aerodynamics 2022年第1期4卷 327-340页

作者： Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Key Laboratory of Machine Perception(Ministry of Education) and School of AIPeking UniversityBeijingChina China Aerodynamics Research and Development Center MianyangChina National Engineering Laboratory for Big Data Analysis and Application Peking UniversityBeijingChina

With the development of deep learning(DL)techniques,many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL *** reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these *** also analyzed the prospects of the development of flow visualization with the help of deep learning.

关键词： Deep learning Flow visualization data management Feature extraction Particle tracing

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A CALIBRATION TEST FOR EVALUATING SET-BASED EPISTEMIC UNCERTAINTY REPRESENTATIONS

arXiv

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arXiv 2025年

作者： Jürgens, Mira Mortier, Thomas Hüllermeier, Eyke Bengs, Viktor Waegeman, Willem Department of Data Analysis and Mathematical Modeling Ghent University Belgium Department of Environment Ghent University Belgium Department of Informatics Munich Center for Machine Learning LMU Munich Germany

The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilistic predictors, also known as credal sets. One popular way of constructing these credal sets is via ensembling or specialized supervised learning methods, where the epistemic uncertainty can be quantified through measures such as the set size or the disagreement among members. In principle, these sets should contain the true data-generating distribution. As a necessary condition for this validity, we adopt the strongest notion of calibration as a proxy. Concretely, we propose a novel statistical test to determine whether there is a convex combination of the set’s predictions that is calibrated in distribution. In contrast to previous methods, our framework allows the convex combination to be instance dependent, recognizing that different ensemble members may be better calibrated in different regions of the input space. Moreover, we learn this combination via proper scoring rules, which inherently optimize for calibration. Building on differentiable, kernel-based estimators of calibration errors, we introduce a nonparametric testing procedure and demonstrate the benefits of capturing instance-level variability on of synthetic and real-world experiments. © 2025, CC BY.

关键词： Self-supervised learning

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ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

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

引用

IEEE International Conference on Big data

作者： Kiran Madhusudhanan Gunnar Behrens Maximilian Stubbemann Lars Schmidt-Thieme Information Systems and Machine Learning Lab VWFS Data Analytics Research Center University Of Hildesheim Data Analytics & AI Engineering Volkswagen Financial Services AG

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

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.

关键词： Industries Accuracy Uncertainty machine learning algorithms Pricing Predictive models Probabilistic logic Prediction algorithms Automobiles Vehicle dynamics

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