Search Results for author:
Found 16 papers, 14 papers with code
Red-Teaming Segment Anything Model
Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications.
Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+4
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases.
Be Careful When Evaluating Explanations Regarding Ground Truth
Evaluating explanations of image classifiers regarding ground truth, e. g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consideration rather than the explanation methods themselves.
survex: an R package for explaining machine learning survival models
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models.
Explaining and visualizing black-box models through counterfactual paths
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable.
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions.
Towards Evaluating Explanations of Vision Transformers for Medical Imaging
Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Time-to-event prediction, e. g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications.
Performance is not enough: the story told by a Rashomon quartet
The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure.
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME.
Graph-guided random forest for gene set selection
To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well.
Do not explain without context: addressing the blind spot of model explanations
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising greater interpretability.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Fooling Partial Dependence via Data Poisoning
We believe this to be the first work using a genetic algorithm for manipulating explanations, which is transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models.
The Grammar of Interactive Explanatory Model Analysis
We conduct a user study to evaluate the usefulness of IEMA, which indicates that an interactive sequential analysis of a model increases the performance and confidence of human decision making.
