b'Marius Kloft'

research

∙ 09/12/2023

Evaluating Dynamic Topic Models

There is a lack of quantitative measures to evaluate the progression of ...

0 Charu James, et al. ∙

research

∙ 08/25/2023

Text Style Transfer Evaluation Using Large Language Models

Text Style Transfer (TST) is challenging to evaluate because the quality...

0 Phil Ostheimer, et al. ∙

research

∙ 06/01/2023

A Call for Standardization and Validation of Text Style Transfer Evaluation

Text Style Transfer (TST) evaluation is, in practice, inconsistent. Ther...

0 Phil Ostheimer, et al. ∙

research

∙ 03/10/2023

Deep Anomaly Detection on Tennessee Eastman Process Data

This paper provides the first comprehensive evaluation and analysis of m...

0 Fabian Hartung, et al. ∙

research

∙ 02/15/2023

Zero-Shot Anomaly Detection without Foundation Models

Anomaly detection (AD) tries to identify data instances that deviate fro...

0 Aodong Li, et al. ∙

research

∙ 02/15/2023

Deep Anomaly Detection under Labeling Budget Constraints

Selecting informative data points for expert feedback can significantly ...

0 Aodong Li, et al. ∙

research

∙ 01/23/2023

Ordinal Regression for Difficulty Estimation of StepMania Levels

StepMania is a popular open-source clone of a rhythm-based video game. A...

0 Billy Joe Franks, et al. ∙

research

∙ 12/16/2022

Generalization Bounds for Inductive Matrix Completion in Low-noise Settings

We study inductive matrix completion (matrix completion with side inform...

0 Antoine Ledent, et al. ∙

research

∙ 10/25/2022

Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings

In this paper, we introduce the Vehicle Claims dataset, consisting of fr...

0 Ajay Chawda, et al. ∙

research

∙ 09/29/2022

Training Normalizing Flows from Dependent Data

Normalizing flows are powerful non-parametric statistical models that fu...

1 Matthias Kirchler, et al. ∙

research

∙ 05/27/2022

Raising the Bar in Graph-level Anomaly Detection

Graph-level anomaly detection has become a critical topic in diverse are...

0 Chen Qiu, et al. ∙

research

∙ 05/23/2022

Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

Traditionally anomaly detection (AD) is treated as an unsupervised probl...

24 Philipp Liznerski, et al. ∙

research

∙ 02/16/2022

Latent Outlier Exposure for Anomaly Detection with Contaminated Data

Anomaly detection aims at identifying data points that show systematic d...

0 Chen Qiu, et al. ∙

research

∙ 02/08/2022

Detecting Anomalies within Time Series using Local Neural Transformations

We develop a new method to detect anomalies within time series, which is...

0 Tim Schneider, et al. ∙

research

∙ 12/08/2021

Trainability for Universal GNNs Through Surgical Randomness

Message passing neural networks (MPNN) have provable limitations, which ...

0 Billy Joe Franks, et al. ∙

research

∙ 09/21/2021

Learning Interpretable Concept Groups in CNNs

We propose a novel training methodology – Concept Group Learning (CGL) –...

10 Saurabh Varshneya, et al. ∙

research

∙ 09/16/2021

Explainability Requires Interactivity

When explaining the decisions of deep neural networks, simple stories ar...

21 Matthias Kirchler, et al. ∙

research

∙ 08/23/2021

Explaining Bayesian Neural Networks

To make advanced learning machines such as Deep Neural Networks (DNNs) m...

17 Kirill Bykov, et al. ∙

research

∙ 05/31/2021

Fine-grained Generalization Analysis of Structured Output Prediction

In machine learning we often encounter structured output prediction prob...

0 Waleed Mustafa, et al. ∙

research

∙ 04/29/2021

Fine-grained Generalization Analysis of Vector-valued Learning

Many fundamental machine learning tasks can be formulated as a problem o...

0 Liang Wu, et al. ∙

research

∙ 03/30/2021

Neural Transformation Learning for Deep Anomaly Detection Beyond Images

Data transformations (e.g. rotations, reflections, and cropping) play an...

0 Chen Qiu, et al. ∙

research

∙ 09/24/2020

A Unifying Review of Deep and Shallow Anomaly Detection

Deep learning approaches to anomaly detection have recently improved the...

49 Lukas Ruff, et al. ∙

research

∙ 09/14/2020

Input Hessian Regularization of Neural Networks

Regularizing the input gradient has shown to be effective in promoting t...

0 Waleed Mustafa, et al. ∙

research

∙ 08/27/2020

Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events

As a vital topic in media content interpretation, video anomaly detectio...

3 Guang Yu, et al. ∙

research

∙ 07/03/2020

Explainable Deep One-Class Classification

Deep one-class classification variants for anomaly detection learn a map...

19 Philipp Liznerski, et al. ∙

research

∙ 06/16/2020

How Much Can I Trust You? – Quantifying Uncertainties in Explaining Neural Networks

Explainable AI (XAI) aims to provide interpretations for predictions mad...

0 Kirill Bykov, et al. ∙

research

∙ 05/30/2020

Rethinking Assumptions in Deep Anomaly Detection

Though anomaly detection (AD) can be viewed as a classification problem ...

51 Lukas Ruff, et al. ∙

research

∙ 04/03/2020

Orthogonal Inductive Matrix Completion

We propose orthogonal inductive matrix completion (OMIC), an interpretab...

0 Antoine Ledent, et al. ∙

research

∙ 01/29/2020

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Activity coefficients, which are a measure of the non-ideality of liquid...

117 Fabian Jirasek, et al. ∙

research

∙ 01/24/2020

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification

Anomaly detection algorithms find extensive use in various fields. This ...

22 Penny Chong, et al. ∙

research

∙ 10/14/2019

Two-sample Testing Using Deep Learning

We propose a two-sample testing procedure based on learned deep neural n...

0 Matthias Kirchler, et al. ∙

research

∙ 10/02/2019

Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator

We study the variance of the REINFORCE policy gradient estimator in envi...

0 James A. Preiss, et al. ∙

research

∙ 06/06/2019

Deep Semi-Supervised Anomaly Detection

Deep approaches to anomaly detection have recently shown promising resul...

12 Lukas Ruff, et al. ∙

research

∙ 05/29/2019

Improved Generalisation Bounds for Deep Learning Through L^∞ Covering Numbers

Using proof techniques involving L^∞ covering numbers, we show generalis...

0 Antoine Ledent, et al. ∙

research

∙ 03/21/2018

Scalable Generalized Dynamic Topic Models

Dynamic topic models (DTMs) model the evolution of prevalent themes in l...

0 Patrick Jähnichen, et al. ∙

research

∙ 02/18/2018

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

We propose an efficient stochastic variational approach to GP classifica...

0 Florian Wenzel, et al. ∙

research

∙ 07/18/2017

Bayesian Nonlinear Support Vector Machines for Big Data

We propose a fast inference method for Bayesian nonlinear support vector...

0 Florian Wenzel, et al. ∙

research

∙ 11/22/2016

Feature Importance Measure for Non-linear Learning Algorithms

Complex problems may require sophisticated, non-linear learning methods ...

0 Marina M. -C. Vidovic, et al. ∙

research

∙ 06/30/2015

Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis

We present a general regularization-based framework for Multi-task learn...

0 Christian Widmer, et al. ∙

research

∙ 04/14/2015

Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are r...

0 Julia E. Vogt, et al. ∙

research

∙ 11/26/2014

Localized Complexities for Transductive Learning

We show two novel concentration inequalities for suprema of empirical pr...

0 Ilya Tolstikhin, et al. ∙

research

∙ 12/16/2011

Insights from Classifying Visual Concepts with Multiple Kernel Learning

Combining information from various image features has become a standard ...

0 Alexander Binder, et al. ∙

research

∙ 03/03/2011

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning

We derive an upper bound on the local Rademacher complexity of ℓ_p-norm ...

0 Marius Kloft, et al. ∙

Marius Kloft

Featured Co-authors