54 citations · 85 across the 19 of their papers we have counts for
13 papers · 1 filter
Mitigating Spurious Correlations in Patch-wise Tumor Classification on High-Resolution Multimodal Images
Ihab Asaad, Maha Shadaydeh, Joachim Denzler
Patch-wise multi-label classification provides an efficient alternative to full pixel-wise segmentation on high-resolution images, particularly when the objective is to determine t…
Group Interventions on Deep Networks for Causal Discovery in Subsystems
Wasim Ahmad, Joachim Denzler, Maha Shadaydeh
Causal discovery uncovers complex relationships between variables, enhancing predictions, decision-making, and insights into real-world systems, especially in nonlinear multivariat…
Gradient Extrapolation for Debiased Representation Learning
Ihab Asaad, Maha Shadaydeh, Joachim Denzler
Machine learning classification models trained with empirical risk minimization (ERM) often inadvertently rely on spurious correlations. When absent in the test data, these uninten…
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
Ferdinand Rewicki, Jakob Gawlikowski, Julia Niebling +1
The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis…
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…
Deep Learning-based Group Causal Inference in Multivariate Time-series
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accu…
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods ma…
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
Violeta Teodora Trifunov, Maha Shadaydeh, Joachim Denzler
Latent variables often mask cause-effect relationships in observational data which provokes spurious links that may be misinterpreted as causal. This problem sparks great interest…
Causal Discovery using Model Invariance through Knockoff Interventions
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causali…
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Estimating causal relations is vital in understanding the complex interactions in multivariate time series. Non-linear coupling of variables is one of the major challenges inaccura…
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz +1
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Th…
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Christian Requena-Mesa, Vitus Benson, Markus Reichstein +2
Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on futu…
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Christian Requena-Mesa, Vitus Benson, Joachim Denzler +2
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (…