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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kalinke, Florian Szabó, Zoltán Institute for Program Structures and Data Organization Karlsruhe Institute of Technology Karlsruhe Germany Department of Statistics London School of Economics London United Kingdom

Kernel techniques are among the most influential approaches in data science and statistics. Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is capable of encoding the independence of M ≥ 2 random variables. Probably the most widespread independence measure relying on kernels is the so-called Hilbert-Schmidt independence criterion (HSIC;also referred to as distance covariance in the statistics literature). Despite various existing HSIC estimators designed since its introduction close to two decades ago, the fundamental question of the rate at which HSIC can be estimated is still open. In this work, we prove that the minimax optimal rate of HSIC estimation on Rd for Borel measures containing the Gaussians with continuous bounded translation-invariant characteristic kernels is (Equation Presented). Specifically, our result implies the optimality in the minimax sense of many of the most-frequently used estimators (including the U-statistic, the V-statistic, and the Nyström-based one) on Rd © 2024 Neural information processing systems foundation. All rights reserved.

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How Domain Knowledge can Improve Machine Learning Surrogates for Manufacturing Process Optimization – a Comparative Study

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Procedia CIRP 2024年 130卷 145-153页

作者： Bela H. Böhnke Aleksandr Eismont Clemens Zimmerling Luise Kärger Klemens Böhm Institute for Program Structures and Data Organization (IPD) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Vehicle System Technology - Lightweight Engineering (FAST-LB) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

In various industries, optimizing manufacturing parameters is vital for the efficient production of high-quality products. Traditional methods involve costly production trials and process tuning – particularly when dealing with complex processes and materials such as composites. High-fidelity simulations offer a cost-effective alternative. However, they can be computationally intensive, which often renders them impracticable for iterative optimization. Surrogate model-based optimization (SuMO) provides a solution by using efficient, data-driven approximations. However, existing approaches often overlook valuable domain knowledge, such as material behavior, spatial relationships and optimization objective. We investigate different types of knowledge varying in complexity, difficulty to incorporate and transferability to other domains. In numerical studies on composite manufacturing – specifically, textile draping – we demonstrate that integrating such domain knowledge improves prediction accuracy, reduces optimization iterations, and enhances overall outcomes.

关键词： Surrogate Modelling Manufacturing Process Optimization Domain-informed Machine Learning Finite-Element-Simulation

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The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels

arXiv

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arXiv 2024年

Kernel techniques are among the most influential approaches in data science and statistics. Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is capable of encoding the independence of M ≥ 2 random variables. Probably the most widespread independence measure relying on kernels is the so-called Hilbert-Schmidt independence criterion (HSIC;also referred to as distance covariance in the statistics literature). Despite various existing HSIC estimators designed since its introduction close to two decades ago, the fundamental question of the rate at which HSIC can be estimated is still open. In this work, we prove that the minimax optimal rate of HSIC estimation on Rd for Borel measures containing the Gaussians with continuous bounded translation-invariant characteristic kernels is O(n−1/2). Specifically, our result implies the optimality in the minimax sense of many of the most-frequently used estimators (including the U-statistic, the V-statistic, and the Nyström-based one) on RdMSC Codes 62C20, 46E22, 47B32, 94A15, 62G10 Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Nyström M-Hilbert-Schmidt Independence Criterion

arXiv

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arXiv 2023年

Kernel techniques are among the most popular and powerful approaches of data science. Among the key features that make kernels ubiquitous are (i) the number of domains they have been designed for, (ii) the Hilbert structure of the function class associated to kernels facilitating their statistical analysis, and (iii) their ability to represent probability distributions without loss of information. These properties give rise to the immense success of Hilbert-Schmidt independence criterion (HSIC) which is able to capture joint independence of random variables under mild conditions, and permits closed-form estimators with quadratic computational complexity (w.r.t. the sample size). In order to alleviate the quadratic computational bottleneck in large-scale applications, multiple HSIC approximations have been proposed, however these estimators are restricted to M "2 random variables, do not extend naturally to the M ě 2 case, and lack theoretical guarantees. In this work, we propose an alternative Nyström-based HSIC estimator which handles the M ě 2 case, prove its consistency, and demonstrate its applicability in multiple contexts, including synthetic examples, dependency testing of media annotations, and causal *** Codes 46E22, 94A17 Copyright © 2023, The Authors. All rights reserved.

关键词： Random variables

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Finite-time analysis of globally nonstationary multi-armed bandits

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 5481-5536页

作者： Junpei Komiyama Edouard Fouché Junya Honda Stern School of Business New York University Institute for Program Structures and Data Organization Karlsruhe Institute of Technology Germany Department of Systems Science Graduate School of Informatics Kyoto University Japan Center for Advanced Intelligence Project RIKEN Japan

We consider nonstationary multi-armed bandit problems where the model parameters of the arms change over time. We introduce the adaptive resetting bandit (ADR-bandit), a bandit algorithm class that leverages adaptive windowing techniques from literature on data streams. We first provide new guarantees on the quality of estimators resulting from adaptive windowing techniques, which are of independent interest. Furthermore, we conduct a finite-time analysis of ADR-bandit in two typical environments: an abrupt environment where changes occur instantaneously and a gradual environment where changes occur progressively. We demonstrate that ADR-bandit has nearly optimal performance when abrupt or gradual changes occur in a coordinated manner that we call global changes. We demonstrate that forced exploration is unnecessary when we assume such global changes. Unlike the existing nonstationary bandit algorithms, ADR-bandit has optimal performance in stationary environments as well as nonstationary environments with global changes. Our experiments show that the proposed algorithms outperform the existing approaches in synthetic and real-world environments.

关键词： multi-armed bandits adaptive windows nonstationary bandits changepoint detection sequential learning

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At your command! An empirical study on how laypersons teach robots new functions 14

At your command! An empirical study on how laypersons teach ...

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14th IEEE International Conference on Semantic Computing, ICSC 2020

作者： Weigelt, Sebastian Steurer, Vanessa Tichy, Walter F. Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Karlsruhe Germany

ISBN: (纸本)9781728163321

Even though intelligent systems such as Siri or Google Assistant are enjoyable (and useful) dialog partners, users can only access predefined functionality. Enabling end-users to extend the functionality of intelligent systems will be the next big thing. To promote research in this area we carried out an empirical study on how laypersons teach robots new functions by means of natural language instructions. The result is a labeled corpus consisting of 3168 submissions given by 870 subjects. The analysis of the dataset revealed that many participants used certain wordings to express their wish to teach new functionality;two corresponding trigrams are among the most frequent. On the contrary, more than one third (37%) did not verbalize the teaching intent at all. We labeled the semantic constituents in the utterances: declaration (including the name of the function) and intermediate steps. © 2020 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Intelligent systems

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Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations

Improving Traceability Link Recovery Using Fine-grained Requ...

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International Conference on Software Maintenance (ICSM)

作者： Tobias Hey Fei Chen Sebastian Weigelt Walter F. Tichy Karlsruhe Institute of Technology (KIT) Institute for Program Structures and Data Organization Karlsruhe Germany

Traceability information is a fundamental prerequisite for many essential software maintenance and evolution tasks, such as change impact and software reusability analyses. However, manually generating traceability information is costly and error-prone. Therefore, researchers have developed automated approaches that utilize textual similarities between artifacts to establish trace links. These approaches tend to achieve low precision at reasonable recall levels, as they are not able to bridge the semantic gap between high-level natural language requirements and code. We propose to overcome this limitation by leveraging fine-grained, method and sentence level, similarities between the artifacts for traceability link recovery. Our approach uses word embeddings and a Word Mover's Distance-based similarity to bridge the semantic gap. The fine-grained similarities are aggregated according to the artifacts structure and participate in a majority vote to retrieve coarse-grained, requirement-to-class, trace links. In a comprehensive empirical evaluation, we show that our approach is able to outperform state-of-the-art unsupervised traceability link recovery approaches. Additionally, we illustrate the benefits of fine-grained structural analyses to word embedding-based trace link generation.

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At your command! an empirical study on how laypersons teach robots new functions

arXiv

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arXiv 2020年

作者： Weigelt, Sebastian Steurer, Vanessa Tichy, Walter F. Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Karlsruhe Germany

Even though intelligent systems such as Siri or Google Assistant are enjoyable (and useful) dialog partners, users can only access predefined functionality. Enabling end-users to extend the functionality of intelligent systems will be the next big thing. To promote research in this area we carried out an empirical study on how laypersons teach robots new functions by means of natural language instructions. The result is a labeled corpus consisting of 3168 submissions given by 870 *** analysis of the dataset revealed that many participants used certain wordings to express their wish to teach new functionality;two corresponding trigrams are among the most frequent. On the contrary, more than one third (36.93%) did not verbalize the teaching intent at all. We labeled the semantic constituents in the utterances: declaration (including the name of the function) and intermediate steps. The full corpus is publicly available: http://***/10.21227/zecn-6c61 Copyright © 2020, The Authors. All rights reserved.

关键词： Intelligent systems

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At Your Command! An Empirical Study on How Laypersons Teach Robots New Functions

At Your Command! An Empirical Study on How Laypersons Teach ...

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International Conference on Semantic Computing

作者： Sebastian Weigelt Vanessa Steurer Walter F. Tichy Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Karlsruhe Germany

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

Even though intelligent systems such as Siri or Google Assistant are enjoyable (and useful) dialog partners, users can only access predefined functionality. Enabling end-users to extend the functionality of intelligent systems will be the next big thing. To promote research in this area we carried out an empirical study on how laypersons teach robots new functions by means of natural language instructions. The result is a labeled corpus consisting of 3168 submissions given by 870 subjects. The analysis of the dataset revealed that many participants used certain wordings to express their wish to teach new functionality; two corresponding trigrams are among the most frequent. On the contrary, more than one third (37%) did not verbalize the teaching intent at all. We labeled the semantic constituents in the utterances: declaration (including the name of the function) and intermediate steps. The full corpus is publicly available: http://***/10.21227/zecn-6c61.

关键词： Education Semantics Intelligent systems Task analysis Vocabulary programming Humanoid robots

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Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems

Roger that! Learning How Laypersons Teach New Functions to I...

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International Conference on Semantic Computing

作者： Sebastian Weigelt Vanessa Steurer Tobias Hey Walter F. Tichy Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Karlsruhe Germany

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

Intelligent systems are rather smart today but still limited to built-in functionality. To break through this barrier, future systems must allow users to easily adapt the system by themselves. For humans the most natural way to communicate is talking. But what if users want to extend the systems' functionality with nothing but natural language? Then intelligent systems must understand how laypersons teach new skills. To grasp the semantics of such teaching sequences, we have defined a hierarchical classification task. On the first level, we consider the existence of a teaching intent in an utterance; on the second, we classify the distinct semantic parts of teaching sequences: declaration of a new function, specification of intermediate steps, and superfluous information. We evaluate twelve machine learning techniques with multiple configurations tailored to this task ranging from classical approaches such as naïve-bayes to modern techniques such as bidirectional LSTMs and task-oriented adaptations. On the first level convolutional neural networks achieve the best accuracy (96.6%). For the second task, bidirectional LSTMs are the most accurate (98.8%). With the additional adaptations we are able to improve both classifications distinctly (up to 1.8%).

关键词： Task analysis Semantics Neural networks Machine learning Intelligent systems Training

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