31 citations · 61 across the 22 of their papers we have counts for
10 papers · 1 filter
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
Andrej Tschalzev, Sascha Marton, Stefan Lüdtke +2
Tabular data is prevalent in real-world machine learning applications, and new models for supervised learning of tabular data are frequently proposed. Comparative studies assessing…
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods
Andrej Tschalzev, Paul Nitschke, Lukas Kirchdorfer +3
Neural networks often assume independence among input data samples, disregarding correlations arising from inherent clustering patterns in real-world datasets (e.g., due to differe…
History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
Julia Gastinger, Christian Meilicke, Federico Errica +3
Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evalu…
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Sascha Marton, Stefan Lüdtke, Christian Bartelt +1
Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, t…
Learning Disentangled Discrete Representations
David Friede, Christian Reimers, Heiner Stuckenschmidt +1
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations…
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Sascha Marton, Stefan Lüdtke, Christian Bartelt +1
Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization pr…
Outlier Explanation via Sum-Product Networks
Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
Outlier explanation is the task of identifying a set of features that distinguish a sample from normal data, which is important for downstream (human) decision-making. Existing met…
Explaining Neural Networks without Access to Training Data
Sascha Marton, Stefan Lüdtke, Christian Bartelt +2
We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues. Recently, $\math…
Exchangeability-Aware Sum-Product Networks
Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence. On the ot…
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
Jovita Lukasik, David Friede, Arber Zela +2
Neural architecture search (NAS) has recently been addressed from various directions, including discrete, sampling-based methods and efficient differentiable approaches. While the…