research
∙
08/04/2023
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE...
share
research
∙
07/25/2023
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
Counterfactual Explanations (CEs) are an important tool in Algorithmic R...
research
∙
06/12/2023
Gaussian Membership Inference Privacy
We propose a new privacy notion called f-Membership Inference Privacy (f...
research
∙
03/14/2023
Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions
As input data distributions evolve, the predictive performance of machin...
research
∙
11/17/2022
Expert Selection in Distributed Gaussian Processes: A Multi-label Classification Approach
By distributing the training process, local approximation reduces the co...
research
∙
11/03/2022
Decomposing Counterfactual Explanations for Consequential Decision Making
The goal of algorithmic recourse is to reverse unfavorable decisions (e....
research
∙
10/25/2022
I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization
To grant users greater authority over their personal data, policymakers ...
research
∙
10/22/2022
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
As input data distributions evolve, the predictive performance of machin...
research
∙
10/20/2022
Towards Human-centered Explainable AI: User Studies for Model Explanations
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expandi...
research
∙
09/06/2022
Change Detection for Local Explainability in Evolving Data Streams
As complex machine learning models are increasingly used in sensitive ap...
research
∙
08/30/2022
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
As machine learning (ML) models are increasingly being deployed in high-...
research
∙
06/28/2022
Disentangling Embedding Spaces with Minimal Distributional Assumptions
Interest in understanding and factorizing learned embedding spaces is gr...
research
∙
06/08/2022
Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation
With increasing digitalization, Artificial Intelligence (AI) is becoming...
research
∙
04/28/2022
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
Due to the unspecified and dynamic nature of data streams, online machin...
research
∙
03/30/2022
Dynamic Model Tree for Interpretable Data Stream Learning
Data streams are ubiquitous in modern business and society. In practice,...
research
∙
03/13/2022
Algorithmic Recourse in the Face of Noisy Human Responses
As machine learning (ML) models are increasingly being deployed in high-...
research
∙
02/07/2022
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes
Distributed Gaussian process (DGP) is a popular approach to scale GP to ...
research
∙
08/02/2021
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Counterfactual explanations provide means for prescriptive model explana...
research
∙
02/03/2021
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types
We present TEyeD, the world's largest unified public data set of eye ima...
research
∙
02/02/2021
Gaussian Experts Selection using Graphical Models
Local approximations are popular methods to scale Gaussian processes (GP...
research
∙
01/04/2021
On Baselines for Local Feature Attributions
High-performing predictive models, such as neural nets, usually operate ...
research
∙
10/29/2020
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
The last decade has witnessed a rapid growth of the field of exoplanet d...
research
∙
10/19/2020
Learning Parameter Distributions to Detect Concept Drift in Data Streams
Data distributions in streaming environments are usually not stationary....
research
∙
10/17/2020
Aggregating Dependent Gaussian Experts in Local Approximation
Distributed Gaussian processes (DGPs) are prominent local approximation ...
research
∙
06/23/2020
On Counterfactual Explanations under Predictive Multiplicity
Counterfactual explanations are usually obtained by identifying the smal...
research
∙
06/18/2020
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Feature selection can be a crucial factor in obtaining robust and accura...
research
∙
01/14/2020
Bias in Data-driven AI Systems – An Introductory Survey
AI-based systems are widely employed nowadays to make decisions that hav...
research
∙
10/21/2019
Towards User Empowerment
Counterfactual explanations can be obtained by identifying the smallest ...
research
∙
05/24/2019
Training decision trees as replacement for convolution layers
We present an alternative layer to convolution layers in convolutional n...
research
∙
01/29/2019
Validation loss for landmark detection
We present a new loss function for the validation of image landmarks det...
research
∙
09/25/2018
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
Machine learning algorithms aim at minimizing the number of false decisi...
research
∙
07/27/2018
Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery
Latent truth discovery, LTD for short, refers to the problem of aggregat...
research
∙
12/31/2017
Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery
We address the problem of latent truth discovery, LTD for short, where t...
research
∙
10/30/2017
PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection
Real-time, accurate, and robust pupil detection is an essential prerequi...
research
∙
01/19/2016
PupilNet: Convolutional Neural Networks for Robust Pupil Detection
Real-time, accurate, and robust pupil detection is an essential prerequi...
research
∙
07/19/2012
