Author name cluster

Konrad Rieck

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7 papers

2 author rows

ICML Conference 2025 Conference Paper

Adversarial Inputs for Linear Algebra Backends

Jonas Möller
Lukas Pirch
Felix Weissberg
Sebastian Baunsgaard
Thorsten Eisenhofer
Konrad Rieck

Linear algebra is a cornerstone of neural network inference. The efficiency of popular frameworks, such as TensorFlow and PyTorch, critically depends on backend libraries providing highly optimized matrix multiplications and convolutions. A diverse range of these backends exists across platforms, including Intel MKL, Nvidia CUDA, and Apple Accelerate. Although these backends provide equivalent functionality, subtle variations in their implementations can lead to seemingly negligible differences during inference. In this paper, we investigate these minor discrepancies and demonstrate how they can be selectively amplified by adversaries. Specifically, we introduce Chimera examples, inputs to models that elicit conflicting predictions depending on the employed backend library. These inputs can even be constructed with integer values, creating a vulnerability exploitable from real-world input domains. We analyze the prevalence and extent of the underlying attack surface and propose corresponding defenses to mitigate this threat.

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NeurIPS Conference 2025 Conference Paper

Manipulating Feature Visualizations with Gradient Slingshots

Dilyara Bareeva
Marina Höhne
Alexander Warnecke
Lukas Pirch
Klaus-Robert Müller
Konrad Rieck
Sebastian Lapuschkin
Kirill Bykov

Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a given feature. Despite its popularity, the trustworthiness of FV explanations has received limited attention. We introduce Gradient Slingshots, a novel method that enables FV manipulation without modifying model architecture or significantly degrading performance. By shaping new trajectories in off-distribution regions of a feature's activation landscape, we coerce the optimization process to converge to a predefined visualization. We evaluate our approach on several DNN architectures, demonstrating its ability to replace faithful FVs with arbitrary targets. These results expose a critical vulnerability: auditors relying solely on FV may accept entirely fabricated explanations. To mitigate this risk, we propose a straightforward defense and quantitatively demonstrate its effectiveness.

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JMLR Journal 2016 Journal Article

Harry: A Tool for Measuring String Similarity

Konrad Rieck
Christian Wressnegger

Comparing strings and assessing their similarity is a basic operation in many application domains of machine learning, such as in information retrieval, natural language processing and bioinformatics. The practitioner can choose from a large variety of available similarity measures for this task, each emphasizing different aspects of the string data. In this article, we present Harry, a small tool specifically designed for measuring the similarity of strings. Harry implements over 20 similarity measures, including common string distances and string kernels, such as the Levenshtein distance and the Subsequence kernel. The tool has been designed with efficiency in mind and allows for multi-threaded as well as distributed computing, enabling the analysis of large data sets of strings. Harry supports common data formats and thus can interface with analysis environments, such as Matlab, Pylab and Weka. [abs] [ pdf ][ bib ] [ code ] [ webpage ] &copy JMLR 2016. ( edit, beta )

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JMLR Journal 2012 Journal Article

Sally: A Tool for Embedding Strings in Vector Spaces

Konrad Rieck
Christian Wressnegger
Alexander Bikadorov

Strings and sequences are ubiquitous in many areas of data analysis. However, only few learning methods can be directly applied to this form of data. We present Sally, a tool for embedding strings in vector spaces that allows for applying a wide range of learning methods to string data. Sally implements a generalized form of the bag-of-words model, where strings are mapped to a vector space that is spanned by a set of string features, such as words or n-grams of words. The implementation of Sally builds on efficient string algorithms and enables processing millions of strings and features. The tool supports several data formats and is capable of interfacing with common learning environments, such as Weka, Shogun, Matlab, or Pylab. Sally has been successfully applied for learning with natural language text, DNA sequences and monitored program behavior. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2012. ( edit, beta )

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JMLR Journal 2010 Journal Article

Approximate Tree Kernels

Konrad Rieck
Tammo Krueger
Ulf Brefeld
Klaus-Robert Müller

Convolution kernels for trees provide simple means for learning with tree-structured data. The computation time of tree kernels is quadratic in the size of the trees, since all pairs of nodes need to be compared. Thus, large parse trees, obtained from HTML documents or structured network data, render convolution kernels inapplicable. In this article, we propose an effective approximation technique for parse tree kernels. The approximate tree kernels (ATKs) limit kernel computation to a sparse subset of relevant subtrees and discard redundant structures, such that training and testing of kernel-based learning methods are significantly accelerated. We devise linear programming approaches for identifying such subsets for supervised and unsupervised learning tasks, respectively. Empirically, the approximate tree kernels attain run-time improvements up to three orders of magnitude while preserving the predictive accuracy of regular tree kernels. For unsupervised tasks, the approximate tree kernels even lead to more accurate predictions by identifying relevant dimensions in feature space. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

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JMLR Journal 2008 Journal Article

Linear-Time Computation of Similarity Measures for Sequential Data

Konrad Rieck
Pavel Laskov

Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of similarity measures for sequences, covering various kernel, distance and non-metric similarity functions. The basis for comparison is embedding of sequences using a formal language, such as a set of natural words, k -grams or all contiguous subsequences. As realizations of the framework we provide linear-time algorithms of different complexity and capabilities using sorted arrays, tries and suffix trees as underlying data structures. Experiments on data sets from bioinformatics, text processing and computer security illustrate the efficiency of the proposed algorithms---enabling peak performances of up to 10 6 pairwise comparisons per second. The utility of distances and non-metric similarity measures for sequences as alternatives to string kernels is demonstrated in applications of text categorization, network intrusion detection and transcription site recognition in DNA. [abs] [ pdf ][ bib ] &copy JMLR 2008. ( edit, beta )

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NeurIPS Conference 2006 Conference Paper

Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

Konrad Rieck
Pavel Laskov
Sören Sonnenburg

We propose a generic algorithm for computation of similarity measures for se- quential data. The algorithm uses generalized sufﬁx trees for efﬁcient calculation of various kernel, distance and non-metric similarity functions. Its worst-case run-time is linear in the length of sequences and independent of the underlying embedding language, which can cover words, k-grams or all contained subse- quences. Experiments with network intrusion detection, DNA analysis and text processing applications demonstrate the utility of distances and similarity coefﬁ- cients for sequences as alternatives to classical kernel functions.

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