Prof. Dr.-Ing.

Bauckhage, Christian

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Now showing 1 - 10 of 13
  • Publication
    From Open Set Recognition Towards Robust Multi-class Classification
    ( 2022-09-15)
    Lübbering, Max
    ;
    Gebauer, Michael
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    The challenges and risks of deploying deep neural networks (DNNs) in the open-world are often overlooked and potentially result in severe outcomes. With our proposed informer approach, we leverage autoencoder-based outlier detectors with their sensitivity to epistemic uncertainty by ensembling multiple detectors each learning a different one-vs-rest setting. Our results clearly show informer’s superiority compared to DNN ensembles, kernel-based DNNs, and traditional multi-layer perceptrons (MLPs) in terms of robustness to outliers and dataset shift while maintaining a competitive classification performance. Finally, we show that informer can estimate the overall uncertainty within a prediction and, in contrast to any of the other baselines, break the uncertainty estimate down into aleatoric and epistemic uncertainty. This is an essential feature in many use cases, as the underlying reasons for the uncertainty are fundamentally different and can require different actions.
  • Publication
    Anonymization of German financial documents using neural network-based language models with contextual word representations
    ( 2022-03)
    Biesner, David
    ;
    Ramamurthy, Rajkumar
    ;
    Loitz, Rüdiger
    ;
    Lübbering, Max
    ;
    Hillebrand, Lars Patrick
    ;
    Ladi, Anna
    ;
    Stenzel, Robin
    ;
    Pielka, Maren
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    The automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we develop an anonymization method for German financial and legal documents using state-of-the-art natural language processing methods based on recurrent neural nets and transformer architectures. We present a web-based application to anonymize financial documents and a large-scale evaluation of different deep learning techniques.
  • Publication
    Bounding open space risk with decoupling autoencoders in open set recognition
    ( 2022)
    Lübbering, Max
    ;
    Gebauer, M.
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    One-vs-Rest (OVR) classification aims to distinguish a single class of interest (COI) from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class is extended from the classes observed during training to unseen and possibly unrelated classes, a setting referred to as open set recognition (OSR). In this work, we propose a novel architecture, namely decoupling autoencoder (DAE), which provides a proven upper bound on the open space risk and minimizes open space risk via a dedicated training routine. Our method is benchmarked within three different scenarios, each isolating different aspects of OSR, namely plain classification, outlier detection, and dataset shift. The results conclusively show that DAE achieves robust performance across all three tasks. This level of cross-task robustness is not observed for any of the seven potent baselines from the OSR, OVR, outlier detection, and ensembling domain which, apart from ATA (Lübbering et al., From imbalanced classification to supervised outlier detection problems: adversarially trained auto encoders. In: Artificial neural networks and machine learning-ICANN 2020, 2020), tend to fail on either one of the tasks. Similar to DAE, ATA is based on autoencoders and facilitates the reconstruction error to predict the inlierness of a sample. However unlike DAE, it does not provide any uncertainty scores and therefore lacks rudimentary means of interpretation. Our adversarial robustness and local stability results further support DAE's superiority in the OSR setting, emphasizing its applicability in safety-critical systems.
  • Publication
    Decoupling Autoencoders for Robust One-vs-Rest Classification
    ( 2021-10-20)
    Lübbering, Max
    ;
    Gebauer, Michael
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    One-vs-Rest (OVR) classification aims to distinguish a single class of interest from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class extends from the classes observed during training to unseen and possibly unrelated classes. In this work, we propose a novel architecture, namely Decoupling Autoencoder (DAE) to tackle the common issue of robustness w.r.t. out-of-distribution samples which is prevalent in classifiers such as multi-layer perceptrons (MLP) and ensemble architectures. Experiments on plain classification, outlier detection, and dataset shift tasks show DAE to achieve robust performance across these tasks compared to the baselines, which tend to fail completely, when exposed to dataset shift. W hile DAE and the baselines yield rather uncalibrated predictions on the outlier detection and dataset shift task, we found that DAE calibration is more stable across all tasks. Therefore, calibration measures applied to the classification task could also improve the calibration of the outlier detection and dataset shift scenarios for DAE.
  • Publication
    Automatic Indexing of Financial Documents via Information Extraction
    ( 2021)
    Ramamurthy, Rajkumar
    ;
    Lübbering, Max
    ;
    Bell , Thiago
    ;
    Gebauer, Michael
    ;
    Ulusay, Bilge
    ;
    Uedelhoven, Daniel
    ;
    Dilmaghani, Tim
    ;
    Loitz, Rüdiger
    ;
    Pielka, Maren
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    The problem of extracting information from large volumes of unstructured documents is pervasive in the domain of financial business. Enterprises and investors need automatic methods that can extract information from these documents, particularly for indexing and efficiently retrieving information. To this end, we present a scalable end-to-end document processing system for indexing and information retrieval from large volumes of financial documents. While we show our system works for the use case of financial document processing, the entire system itself is agnostic of the document type and machine learning model type. Thus, it can be applied to any large-scale document processing task involving domain-specific extractors.
  • Publication
    Utilizing Representation Learning for Robust Text Classification Under Datasetshift
    ( 2021)
    Lübbering, Max
    ;
    Gebauer, Michael
    ;
    Ramamurthy, Rajkumar
    ;
    Pielka, Maren
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    Within One-vs-Rest (OVR) classification, a classifier differentiates a single class of interest (COI) from the rest, i.e. any other class. By extending the scope of the rest class to corruptions (dataset shift), aspects of outlier detection gain relevancy. In this work, we show that adversarially trained autoencoders (ATA) representative of autoencoder-based outlier detection methods, yield tremendous robustness improvements over traditional neural network methods such as multi-layer perceptrons (MLP) and common ensemble methods, while maintaining a competitive classification performance. In contrast, our results also reveal that deep learning methods solely optimized for classification, tend to fail completely when exposed to dataset shift.
  • Publication
    Supervised autoencoder variants for end to end anomaly detection
    ( 2021)
    Lübbering, Max
    ;
    Gebauer, Michael
    ;
    Ramamurthy, Rajkumar
    ;
    Sifa, Rafet
    ;
    Bauckhage, Christian
    Despite the success of deep learning in various domains such as natural language processing, speech recognition, and computer vision, learning from a limited amount of samples and generalizing to unseen data still pose challenges. Notably, in the tasks of outlier detection and imbalanced dataset classification, the label of interest is either scarce or its distribution is skewed, causing aggravated generalization problems. In this work, we pursue the direction of multi-task learning, specifically the idea of using supervised autoencoders (SAE), which allows us to combine unsupervised and supervised objectives in an end to end fashion. We extend this approach by introducing an adversarial supervised objective to enrich the representations which are learned for the classification task. We conduct thorough experiments on a broad range of tasks, including outlier detection, novelty detection, and imbalanced classification, and study the efficacy of our method against standard baselines using autoencoders. Our work empirically shows that the SAE methods outperform one class autoencoders, adversarially trained autoencoders and multi layer perceptrons in terms of AUPR score comparison. Additionally, our analysis of the obtained representations suggests that the adversarial reconstruction loss functions enforce the encodings to separate into class-specific clusters, which was not observed for non-adversarial reconstruction loss functions.
  • Publication
    Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis
    ( 2021)
    Lübbering, Max
    ;
    Pielka, Maren
    ;
    Das, Kajaree
    ;
    Gebauer, Michael
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    We present a comparative study on toxicity detection, focusing on the problem of identifying toxicity types of low prevalence and possibly even unobserved at training time. For this purpose, we train our models on a dataset that contains only a weak type of toxicity, and test whether they are able to generalize to more severe toxicity types. We find that representation learning and ensembling exceed the classification performance of simple classifiers on toxicity detection, while also providing significantly better generalization and robustness. All models benefit from a larger training set size, which even extends to the toxicity types unseen during training.
  • Publication
    Novelty-Guided Reinforcement Learning via Encoded Behaviors
    ( 2020-09-28)
    Ramamurthy, Rajkumar
    ;
    Sifa, Rafet
    ;
    Lübbering, Max
    ;
    Bauckhage, Christian
    Despite the successful application of Deep Reinforcement Learning (DRL) in a wide range of complex tasks, agents either often learn sub-optimal behavior due to the sparse/deceptive nature of rewards or require a lot of interactions with the environment. Recent methods combine a class of algorithms known as Novelty Search (NS), which circumvents this problem by encouraging exploration towards novel behaviors. Even without exploiting any environment rewards, they are capable of learning skills that yield competitive results in several tasks. However, to assign novelty scores to policies, these methods rely on neighborhood models that store behaviors in an archive set. Hence they do not scale and generalize to complex tasks requiring too many policy evaluations. Addressing these challenges, we propose a function approximation paradigm to instead learn sparse representations of agent behaviors using auto-encoders, which are later used to assign novelty scores to policies. E xperimental results on benchmark tasks suggest that this way of novelty-guided exploration is a viable alternative to classic novelty search methods.
  • Publication
    Guided Reinforcement Learning via Sequence Learning
    ( 2020)
    Ramamurthy, Rajkumar
    ;
    Sifa, Rafet
    ;
    Lübbering, Max
    ;
    Bauckhage, Christian
    Applications of Reinforcement Learning (RL) suffer from high sample complexity due to sparse reward signals and inadequate exploration. Novelty Search (NS) guides as an auxiliary task, in this regard to encourage exploration towards unseen behaviors. However, NS suffers from critical drawbacks concerning scalability and generalizability since they are based off instance learning. Addressing these challenges, we previously proposed a generic approach using unsupervised learning to learn representations of agent behaviors and use reconstruction losses as novelty scores. However, it considered only fixed-length sequences and did not utilize sequential information of behaviors. Therefore, we here extend this approach by using sequential auto-encoders to incorporate sequential dependencies. Experimental results on benchmark tasks show that this sequence learning aids exploration outperforming previous novelty search methods.