200 citations · 675 across the 45 of their papers we have counts for
47 papers
Information Plane Analysis for Dropout Neural Networks
Linara Adilova, Bernhard C. Geiger, Asja Fischer
The information-theoretic framework promises to explain the predictive power of neural networks. In particular, the information plane analysis, which measures mutual information (M…
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory
Maximilian B. Toller, Bernhard C. Geiger, Roman Kern
Rate-distortion theory-based outlier detection builds upon the rationale that a good data compression will encode outliers with unique symbols. Based on this rationale, we propose…
A conceptual model for leaving the data-centric approach in machine learning
Sebastian Scher, Bernhard Geiger, Simone Kopeinik +2
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been cha…
Robust Bayesian Target Value Optimization
Johannes G. Hoffer, Sascha Ranftl, Bernhard C. Geiger
We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in th…
Trustworthy Representation Learning via Information Funnels and Bottlenecks
João Machado de Freitas, Bernhard C. Geiger
Ensuring trustworthiness in machine learning -- by balancing utility, fairness, and privacy -- remains a critical challenge, particularly in representation learning. In this work,…
Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering
João Machado de Freitas, Sebastian Berg, Bernhard C. Geiger +1
In this paper, we frame homogeneous-feature multi-task learning (MTL) as a hierarchical representation learning problem, with one task-agnostic and multiple task-specific latent re…