4 citations · 6 across the 5 of their papers we have counts for
6 papers
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?
Ingo Ziegler, Bolei Ma, Ercong Nie +4
Epitope vaccines are a promising direction to enable precision treatment for cancer, autoimmune diseases, and allergies. Effectively designing such vaccines requires accurate predi…
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…
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization
Daniel Schalk, Bernd Bischl, David Rügamer
Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability. CWB…
Automatic Componentwise Boosting: An Interpretable AutoML System
Stefan Coors, Daniel Schalk, Bernd Bischl +1
In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model e…
Inference for -Boosting
David Rügamer, Sonja Greven
We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as -Bo…