Our preprint "Fair Interpretable Representation Learning with Correction Vectors" (by Cerrato, Coronel, Koppel, Segner, Esposito and Kramer) has been featured by the Montreal AI Ethics Institute. You may read the research summary here.
Our paper "Fair Interpretable Representation Learning with Correction Vectors" has been featured by the Montreal AI Ethics Institute
Our paper "Learning to Rank Higgs Boson Candidates", which is a joint work of Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Christian Schmitt and Stefan Kramer has been accepted at Nature Scientific Reports.
In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion. In this paper, we propose to use algorithms for learning to rank, i.e., to rank events into a sorting order, first signal, then background, instead of algorithms for the classification into two classes, for this task. The fact that training is then performed on pairwise comparisons of signal and background events can effectively increase the amount of training data due to the quadratic number of possible combinations. This makes it robust to unbalanced data set scenarios and can improve the overall performance compared to pointwise models like the state-of-the-art boosted decision tree approach. In this work we compare our pairwise neural network algorithm, which is a combination of a convolutional neural network and the DirectRanker, with convolutional neural networks, multilayer perceptrons or boosted decision trees, which are commonly used algorithms in multiple Higgs production channels. Furthermore, we use so-called transfer learning techniques to improve overall performance on different data types.
Our short paper "Ranking Creative Language Characteristics in Small Data Scenarios" has been accepted at ICCC’22
Our short paper "Ranking Creative Language Characteristics in Small Data Scenarios", which is a joint work of Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe, Iryna Gurevych, Stefan Kramer has been accepted at ICCC'22.
The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require substantial amounts of labeled data that are difficult and expensive to obtain for new domains, languages and creative characteristics. A recent neural approach, DirectRanker, reduces the amount of training data needed but has not previously been used to rank creative text. We therefore adapt DirectRanker to provide a new deep model for ranking creative language with small numbers of training instances, and compare it with a Bayesian approach, Gaussian process preference learning (GPPL), which was previously shown to work well with sparse data. Our experiments with short creative language texts show the effectiveness of DirectRanker even with small training datasets. Combining DirectRanker with GPPL outperforms the previous state of the art on humor and metaphor novelty tasks, increasing Spearman's ρ by 25% and 29% on average. Furthermore, we provide a possible application to validate jokes in the process of creativity generation.
Our paper "Deep Unsupervised Identification of Selected Genes and SNPs in Pool-Seq Data from Evolving Populations" has been accepted as poster presentation at RECOMB-Genetics’22
Our paper "Deep Unsupervised Identification of Selected Genes and SNPs in Pool-Seq Data from Evolving Populations", which was a joint work of Julia Siekiera and Stefan Kramer has been accepted as poster presentation at RECOMB 2022-Genetics.
The exploration of selected single nucleotide polymorphisms (SNPs) to identify genetic diversity between populations under selection pressure is a fundamental task in population genetics. As underlying sequence reads and their alignment are error-prone and univariate statistical solutions like the Cochran-Mantel-Haenszel test (CMH) only take individual positions of the genome into account, the identification of selected SNPs remains a challenging process. Deep learning models, by contrast, are able to consider large input areas to integrate the decision of individual positions in the context of (hidden) neighboring patterns. We suggest an unsupervised deep learning pipeline to detect selected SNPs or genes between different types of population pairs by the application of both active learning and explainable AI methods. To provide a solution for various experimental designs, the effectiveness of direct genomic population comparison and the integration of drift simulation is investigated. In addition, we demonstrate how the extension of an autoencoder architecture can support the mapping of the genotype into a hidden representation upon which optimized selection detection is possible. The performance of the proposed method configurations is investigated on different simulated sequencing pools of individuals (Pool-Seq)datasets of Drosophila melanogaster and compared to an univariate baseline. The evaluation demonstrates that deep neural networks offer the potential to recognize hidden patterns in the allele frequencies of evolved populations and to enhance the information given by univariate statistics.
Our paper "Deep neural networks to recover unknown physical parameters from oscillating time series" has been accepted at PLOS ONE
Our paper "Deep neural networks to recover unknown physical parameters from oscillating time series" (DOI) which was a joint work of Antoine Garcon, Julian Vexler, Dmitry Budker and Stefan Kramer was accepted at PLOS ONE.
Deep neural networks are widely used in pattern-recognition tasks for which a human-comprehensible, quantitative description of the data-generating process, cannot be obtained. While doing so, neural networks often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This may be one of the reasons why neural networks are not yet used extensively in physics-experiment signal processing: physicists generally require their analyses to yield quantitative information about the system they study. In this article we use a deep neural network to disentangle components of oscillating time series. To this aim, we design and train the neural network on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings with true latent-parameters initial guesses, in spite of the neural network needing no initial guesses at all. We then explore various applications in which we believe our architecture could prove useful for time-series processing, when prior knowledge is incomplete. As an example, we employ the neural network as a preprocessing tool to inform the least-square fits when initial guesses are unknown. Moreover, we show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.
Our paper "fair pairwise learning to rank" has been accepted at IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Our paper "fair pairwise learning to rank", which was a joint work of Mattia Cerrato, Marius Köppel, Alexander Segner, Roberto Esposito, and Stefan Kramer, was accepted at IEEE International Conference on Data Science and Advanced Analytics (DSAA).
Abstract: Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why
a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.
In this work we present a family of fair pairwise learning to rank approaches based on Neural Networks, which are able to produce balanced outcomes for underprivileged groups and, at the same time, build fair representations of data, i.e. new vectors having no correlation with regard to a sensitive attribute. We
compare our approaches to recent work dealing with fair ranking and evaluate them using both relevance and fairness metrics. Our results show that the introduced fair pairwise ranking methods compare favorably to other methods when considering the fairness/relevance trade-off.
Dr. Mohammad Sadeq Dousti has joined the Data Mining group and will be mainly working on privacy-preserving data mining. In addition to his research, he will also support our group with the teaching terms.
Stereotypes in Artificial Intelligence
Today, Artificial Intelligence systems are used in many areas and support among other things decision-making processes (e.g. in checking creditworthiness or assessing the probability of a criminal returning to crime). It is precisely these systems that one wishes to be free of prejudice. Unfortunately, however, this does not always correspond to reality. But to what extent do AI systems show prejudice in general and gender bias in particular? The talk focuses on the area of Commonsense Reasoning, which finds an application in the field of automatic assistants such as Siri and Alexa, and considers the question of how prejudice can be measured in this area.
Dr. Claudia Schon is a Klara-Marie-Faßbinder-Gastprofessor at TH Bingen.
The talk will take place on the 31.01.20 at 14:00 in room 05-514.