3 citations · 6 across the 7 of their papers we have counts for
7 papers
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
Gideon Stein, Niklas Penzel, Tristan Piater +1
Causal Discovery (CD) is a powerful framework for scientific inquiry. Yet, its practical adoption is hindered by a reliance on strong, often unverifiable assumptions and a lack of…
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series
Gideon Stein, Maha Shadaydeh, Jan Blunk +2
Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it. Despite this, in-the-…
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out
Tristan Piater, Niklas Penzel, Gideon Stein +1
A substantial body of research has focused on developing systems that assist medical professionals during labor-intensive early screening processes, many based on convolutional dee…
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
Niklas Penzel, Gideon Stein, Joachim Denzler
Deep models trained on large amounts of data often incorporate implicit biases present during training time. If later such a bias is discovered during inference or deployment, it i…
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Gideon Stein, Maha Shadaydeh, Joachim Denzler
Causal discovery from time series data encompasses many existing solutions, including those based on deep learning techniques. However, these methods typically do not endorse one o…
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning
Gideon Stein, Andrey Filchenkov, Arip Asadulaev
Since the publication of the original Transformer architecture (Vaswani et al. 2017), Transformers revolutionized the field of Natural Language Processing. This, mainly due to thei…