170 citations · 334 across the 9 of their papers we have counts for
11 papers
Deep Learning for Survival Analysis: A Review
Simon Wiegrebe, Philipp Kopper, Raphael Sonabend +2
The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstruct…
Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval
Benno Weck, Miguel Pérez Fernández, Holger Kirchhoff +1
We present an analysis of large-scale pretrained deep learning models used for cross-modal (text-to-audio) retrieval. We use embeddings extracted by these models in a metric learni…
Factorized Structured Regression for Large-Scale Varying Coefficient Models
David Rügamer, Andreas Bender, Simon Wiegrebe +4
Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facil…
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
Philipp Kopper, Simon Wiegrebe, Bernd Bischl +2
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications. Despite…
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures
Raphael Sonabend, Andreas Bender, Sebastian Vollmer
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination. This is a non-trivial problem as discrimination measures are the most c…
Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls
Alexander Bauer, André Klima, Jana Gauß +3
Election poll reporting often focuses on mean values and only subordinately discusses the underlying uncertainty. Subsequent interpretations are too often phrased as certain. Moreo…
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
mlr3proba: An R Package for Machine Learning in Survival Analysis
Raphael Sonabend, Franz J. Király, Andreas Bender +2
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R lib…
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rügamer, Fabian Scheipl +1
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, an…
KOALA: A new paradigm for election coverage
Alexander Bauer, Andreas Bender, André Klima +1
Common election poll reporting is often misleading as sample uncertainty is addressed insufficiently or not covered at all. Furthermore, main interest usually lies beyond the simpl…
pammtools: Piece-wise exponential Additive Mixed Modeling tools
Johannes Piller, Fabian Scheipl, Philipp Kopper +1
Piecewise exponential additive mixed models (PAMMs) provide a flexible framework for analyzing censored and truncated time-to-event data, bridging classical hazard-based modeling w…