36 citations · 173 across the 61 of their papers we have counts for
10 papers · 1 filter
Differentiable Sparsity via -Gating: Simple and Versatile Structured Penalization
Chris Kolb, Laetitia Frost, Bernd Bischl +1
Structured sparsity regularization offers a principled way to compact neural networks, but its non-differentiability breaks compatibility with conventional stochastic gradient desc…
TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
Emmanouil Panagiotou, Benoît Ronval, Arjun Roy +4
Ensuring fairness in machine learning remains a significant challenge, as models often inherit biases from their training data. Generative models have recently emerged as a promisi…
Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl +3
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, e…
Overtuning in Hyperparameter Optimization
Lennart Schneider, Bernd Bischl, Matthias Feurer
Hyperparameter optimization (HPO) aims to identify an optimal hyperparameter configuration (HPC) such that the resulting model generalizes well to unseen data. As the expected gene…
Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
Tobias Pielok, Bernd Bischl, David Rügamer
Semi-implicit variational inference (SIVI) is a powerful framework for approximating complex posterior distributions, but training with the Kullback-Leibler (KL) divergence can be…
Revisiting Unbiased Implicit Variational Inference
Tobias Pielok, Bernd Bischl, David Rügamer
Recent years have witnessed growing interest in semi-implicit variational inference (SIVI) methods due to their ability to rapidly generate samples from complex distributions. Howe…
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
The correct way to quantify predictive uncertainty in neural networks remains a topic of active discussion. In particular, it is unclear whether the state-of-the art entropy decomp…
Calibrating LLMs with Information-Theoretic Evidential Deep Learning
Yawei Li, David Rügamer, Bernd Bischl +1
Fine-tuned large language models (LLMs) often exhibit overconfidence, particularly when trained on small datasets, resulting in poor calibration and inaccurate uncertainty estimate…
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
Chris Kolb, Tobias Weber, Bernd Bischl +1
Sparse regularization techniques are well-established in machine learning, yet their application in neural networks remains challenging due to the non-differentiability of penaltie…
Privilege Scores
Ludwig Bothmann, Philip A. Boustani, Jose M. Alvarez +3
Bias-transforming methods of fairness-aware machine learning aim to correct a non-neutral status quo with respect to a protected attribute (PA). Current methods, however, lack an e…