165 citations · 253 across the 16 of their papers we have counts for
14 papers · 1 filter
Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhanc…
Counterfactual Explanation for Regression via Disentanglement in Latent Space
Xuan Zhao, Klaus Broelemann, Gjergji Kasneci
Counterfactual Explanations (CEs) help address the question: How can the factors that influence the prediction of a predictive model be changed to achieve a more favorable outcome…
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
Xuan Zhao, Simone Fabbrizzi, Paula Reyero Lobo +4
The unequal representation of different groups in a sample population can lead to discrimination of minority groups when machine learning models make automated decisions. To addres…
Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, an…
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
Xuan Zhao, Klaus Broelemann, Gjergji Kasneci
Counterfactual Explanations (CEs) are an important tool in Algorithmic Recourse for addressing two questions: 1. What are the crucial factors that led to an automated prediction/de…
Explanation Shift: How Did the Distribution Shift Impact the Model?
Carlos Mougan, Klaus Broelemann, David Masip +3
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then…
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
Carlos Mougan, Klaus Broelemann, Gjergji Kasneci +2
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In the past, predictive performance was considered the key indicator…
Dynamic Model Tree for Interpretable Data Stream Learning
Johannes Haug, Klaus Broelemann, Gjergji Kasneci
Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine lear…
On Counterfactual Explanations under Predictive Multiplicity
Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
Counterfactual explanations are usually obtained by identifying the smallest change made to an input to change a prediction made by a fixed model (hereafter called sparse methods).…
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Johannes Haug, Martin Pawelczyk, Klaus Broelemann +1
Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need…
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
Martin Pawelczyk, Johannes Haug, Klaus Broelemann +1
Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' t…
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
Klaus Broelemann, Gjergji Kasneci
Machine learning algorithms aim at minimizing the number of false decisions and increasing the accuracy of predictions. However, the high predictive power of advanced algorithms co…
Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery
Klaus Broelemann, Gjergji Kasneci
Latent truth discovery, LTD for short, refers to the problem of aggregating ltiple claims from various sources in order to estimate the plausibility of atements about entities. In…
Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery
Klaus Broelemann, Thomas Gottron, Gjergji Kasneci
We address the problem of latent truth discovery, LTD for short, where the goal is to discover the underlying true values of entity attributes in the presence of noisy, conflicting…