190 citations · 634 across the 21 of their papers we have counts for
22 papers
A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction
Zeyang Huang, Angelos Chatzimparmpas, Thomas Höllt +1
Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure. Neighbor embedding methods such…
Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction
Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur +4
While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how different data sources influe…
LangLasso: Interactive Cluster Descriptions through LLM Explanation
Raphael Buchmüller, Dennis Collaris, Linhao Meng +1
Dimensionality reduction is a powerful technique for revealing structure and potential clusters in data. However, as the axes are complex, non-linear combinations of features, they…
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Zeyang Huang, Takanori Fujiwara, Angelos Chatzimparmpas +2
We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more effi…
Visual Analytics for Explainable and Trustworthy Artificial Intelligence
Angelos Chatzimparmpas
Our society increasingly depends on intelligent systems to solve complex problems, ranging from recommender systems suggesting the next movie to watch to AI models assisting in med…
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
Sheng Long, Angelos Chatzimparmpas, Emma Alexander +2
Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-f…
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
Negar Kamali, Karyn Nakamura, Aakriti Kumar +3
Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generat…
Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework
Angelos Chatzimparmpas, Evanthia Dimara
AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are…
How to Distinguish AI-Generated Images from Authentic Photographs
Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas +2
The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between re…
Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren
Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics…
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
Parisa Salmanian, Angelos Chatzimparmpas, Ali Can Karaca +1
Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncov…
Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling
Dongping Zhang, Angelos Chatzimparmpas, Negar Kamali +1
As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the presentation of c…
Pre-registration for Predictive Modeling
Jake M. Hofman, Angelos Chatzimparmpas, Amit Sharma +2
Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field.…
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
Angelos Chatzimparmpas, Rafael M. Martins, Alexandru C. Telea +1
As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and tru…
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
A. Chatzimparmpas, R. Martins, I. Jusufi +3
Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, howe…
MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels
Ilya Ploshchik, Angelos Chatzimparmpas, Andreas Kerren
Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data se…
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
Angelos Chatzimparmpas, Fernando V. Paulovich, Andreas Kerren
Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques…
VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these met…
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data, including complex feature engineering processes…
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an e…
StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher +1
In machine learning (ML), ensemble methods such as bagging, boosting, and stacking are widely-established approaches that regularly achieve top-notch predictive performance. Stacki…
t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range…