3 citations · 5 across the 17 of their papers we have counts for
17 papers
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…
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-…
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…
Regime Identification for Improving Causal Analysis in Non-stationary Timeseries
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Time series data from real-world systems often display non-stationary behavior, indicating varying statistical characteristics over time. This inherent variability poses significan…
Physics Informed Modeling of Ecosystem Respiration via Dynamic Mode Decomposition with Control Input
Maha Shadaydeh, Joachim Denzler, Mirco Migliavacca
Ecosystem respiration (Reco) represents a major component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding o…
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…
Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification
Dimitri Korsch, Maha Shadaydeh, Joachim Denzler
Fine-grained classification is a particular case of a classification problem, aiming to classify objects that share the visual appearance and can only be distinguished by subtle di…
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…
Counterfactual Generation with Knockoffs
Oana-Iuliana Popescu, Maha Shadaydeh, Joachim Denzler
Human interpretability of deep neural networks' decisions is crucial, especially in domains where these directly affect human lives. Counterfactual explanations of already trained…
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study
Maha Shadaydeh, Lea Mueller, Dana Schneider +3
Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of politi…
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
Lea Müller, Maha Shadaydeh, Martin Thümmel +3
Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation. Identifying the direction of influence, or cause-effect relation between…
Image Denoising Via Collaborative Support-Agnostic Recovery
Muzammil Behzad, Mudassir Masood, Tarig Ballal +2
In this paper, we propose a novel image denoising algorithm using collaborative support-agnostic sparse reconstruction. An observed image is first divided into patches. Similarly s…