b'Bernd Bischl'

research

∙ 09/05/2023

Probabilistic Self-supervised Learning via Scoring Rules Minimization

In this paper, we propose a novel probabilistic self-supervised learning...

0 Amirhossein Vahidi, et al. ∙

research

∙ 08/28/2023

Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning

Ensembling a neural network is a widely recognized approach to enhance m...

0 Amirhossein Vahidi, et al. ∙

research

∙ 07/17/2023

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML

Automated machine learning (AutoML) systems commonly ensemble models pos...

0 Lennart Purucker, et al. ∙

research

∙ 07/17/2023

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

We present a model-agnostic framework for jointly optimizing the predict...

0 Lennart Schneider, et al. ∙

research

∙ 07/14/2023

How Different Is Stereotypical Bias Across Languages?

Recent studies have demonstrated how to assess the stereotypical bias in...

0 Ibrahim Tolga Öztürk, et al. ∙

research

∙ 07/07/2023

Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization

This paper introduces a smooth method for (structured) sparsity in ℓ_q a...

0 Chris Kolb, et al. ∙

research

∙ 06/16/2023

ActiveGLAE: A Benchmark for Deep Active Learning with Transformers

Deep active learning (DAL) seeks to reduce annotation costs by enabling ...

0 Lukas Rauch, et al. ∙

research

∙ 06/01/2023

Decomposing Global Feature Effects Based on Feature Interactions

Global feature effect methods, such as partial dependence plots, provide...

0 Julia Herbinger, et al. ∙

research

∙ 05/25/2023

Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction

Undersampling is a common method in Magnetic Resonance Imaging (MRI) to ...

0 Tobias Weber, et al. ∙

research

∙ 05/04/2023

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations

This work introduces interpretable regional descriptors, or IRDs, for lo...

4 Susanne Dandl, et al. ∙

research

∙ 04/13/2023

counterfactuals: An R Package for Counterfactual Explanation Methods

Counterfactual explanation methods provide information on how feature va...

6 Susanne Dandl, et al. ∙

research

∙ 04/06/2023

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

Bayesian inference in deep neural networks is challenging due to the hig...

0 Jonas Gregor Wiese, et al. ∙

research

∙ 03/20/2023

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis

While recent advances in large-scale foundational models show promising ...

0 Tobias Weber, et al. ∙

research

∙ 03/15/2023

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

The field of automated machine learning (AutoML) introduces techniques t...

19 Hilde Weerts, et al. ∙

research

∙ 12/08/2022

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

Modern machine learning models are often constructed taking into account...

0 Matthias Feurer, et al. ∙

research

∙ 11/17/2022

Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods

In this work, we propose an efficient implementation of mixtures of expe...

0 David Rügamer, et al. ∙

research

∙ 10/14/2022

Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models

Various privacy-preserving frameworks that respect the individual's priv...

0 Daniel Schalk, et al. ∙

research

∙ 09/14/2022

Joint Debiased Representation and Image Clustering Learning with Self-Supervision

Contrastive learning is among the most successful methods for visual rep...

8 Shunjie-Fabian Zheng, et al. ∙

research

∙ 09/14/2022

Improved proteasomal cleavage prediction with positive-unlabeled learning

Accurate in silico modeling of the antigen processing pathway is crucial...

4 Emilio Dorigatti, et al. ∙

research

∙ 09/06/2022

Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision

Learning from positive and unlabeled (PU) data is a setting where the le...

7 Emilio Dorigatti, et al. ∙

research

∙ 07/30/2022

HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

Hyperparameter optimization (HPO) is a key component of machine learning...

1 Lennart Schneider, et al. ∙

research

∙ 07/30/2022

Tackling Neural Architecture Search With Quality Diversity Optimization

Neural architecture search (NAS) has been studied extensively and has gr...

0 Lennart Schneider, et al. ∙

research

∙ 07/25/2022

AMLB: an AutoML Benchmark

Comparing different AutoML frameworks is notoriously challenging and oft...

12 Pieter Gijsbers, et al. ∙

research

∙ 06/15/2022

Multi-Objective Hyperparameter Optimization – An Overview

Hyperparameter optimization constitutes a large part of typical modern m...

0 Florian Karl, et al. ∙

research

∙ 06/11/2022

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution

Despite all the benefits of automated hyperparameter optimization (HPO),...

7 Julia Moosbauer, et al. ∙

research

∙ 05/25/2022

Factorized Structured Regression for Large-Scale Varying Coefficient Models

Recommender Systems (RS) pervade many aspects of our everyday digital li...

0 David Rügamer, et al. ∙

research

∙ 05/19/2022

What Is Fairness? Implications For FairML

A growing body of literature in fairness-aware ML (fairML) aspires to mi...

0 Ludwig Bothmann, et al. ∙

research

∙ 05/11/2022

Efficient Automated Deep Learning for Time Series Forecasting

Recent years have witnessed tremendously improved efficiency of Automate...

12 Difan Deng, et al. ∙

research

∙ 04/28/2022

A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models

The goal of Quality Diversity Optimization is to generate a collection o...

0 Lennart Schneider, et al. ∙

research

∙ 03/21/2022

Distributed non-disclosive validation of predictive models by a modified ROC-GLM

Distributed statistical analyses provide a promising approach for privac...

0 Daniel Schalk, et al. ∙

research

∙ 02/16/2022

Cross-Modal Common Representation Learning with Triplet Loss Functions

Common representation learning (CRL) learns a shared embedding between t...

18 Felix Ott, et al. ∙

research

∙ 02/15/2022

REPID: Regional Effect Plots with implicit Interaction Detection

Machine learning models can automatically learn complex relationships, s...

12 Julia Herbinger, et al. ∙

research

∙ 02/14/2022

Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens

Handwriting is one of the most frequently occurring patterns in everyday...

2 Felix Ott, et al. ∙

research

∙ 02/12/2022

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis

Survival analysis (SA) is an active field of research that is concerned ...

0 Philipp Kopper, et al. ∙

research

∙ 01/31/2022

Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection

Pseudo-labeling solutions for positive-unlabeled (PU) learning have the ...

12 Emilio Dorigatti, et al. ∙

research

∙ 01/21/2022

Marginal Effects for Non-Linear Prediction Functions

Beta coefficients for linear regression models represent the ideal form ...

4 Christian A. Scholbeck, et al. ∙

research

∙ 11/29/2021

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

Automated hyperparameter optimization (HPO) has gained great popularity ...

0 Julia Moosbauer, et al. ∙

research

∙ 11/08/2021

Explaining Hyperparameter Optimization via Partial Dependence Plots

Automated hyperparameter optimization (HPO) can support practitioners to...

24 Julia Moosbauer, et al. ∙

research

∙ 10/21/2021

Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation

The application of deep learning in survival analysis (SA) gives the opp...

0 Tobias Weber, et al. ∙

research

∙ 10/21/2021

Survival-oriented embeddings for improving accessibility to complex data structures

Deep learning excels in the analysis of unstructured data and recent adv...

0 Tobias Weber, et al. ∙

research

∙ 10/07/2021

Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization

Componentwise boosting (CWB), also known as model-based boosting, is a v...

0 Daniel Schalk, et al. ∙

research

∙ 09/22/2021

Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images

We propose a Deep Variational Clustering (DVC) framework for unsupervise...

0 Farzin Soleymani, et al. ∙

research

∙ 09/15/2021

Deep Bregman Divergence for Contrastive Learning of Visual Representations

Deep Bregman divergence measures divergence of data points using neural ...

0 Mina Rezaei, et al. ∙

research

∙ 09/12/2021

Automatic Componentwise Boosting: An Interpretable AutoML System

In practice, machine learning (ML) workflows require various different s...

0 Stefan Coors, et al. ∙

research

∙ 09/11/2021

Learning Statistical Representation with Joint Deep Embedded Clustering

One of the most promising approaches for unsupervised learning is combin...

17 Mina Rezaei, et al. ∙

research

∙ 09/08/2021

YAHPO Gym – Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization

When developing and analyzing new hyperparameter optimization (HPO) meth...

0 Florian Pfisterer, et al. ∙

research

∙ 09/03/2021

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process

Scientists and practitioners increasingly rely on machine learning to mo...

1 Christoph Molnar, et al. ∙

research

∙ 07/28/2021

Developing Open Source Educational Resources for Machine Learning and Data Science

Education should not be a privilege but a common good. It should be open...

7 Ludwig Bothmann, et al. ∙

research

∙ 07/13/2021

Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

Most machine learning algorithms are configured by one or several hyperp...

138 Bernd Bischl, et al. ∙

research

∙ 07/04/2021

Mutation is all you need

Neural architecture search (NAS) promises to make deep learning accessib...

0 Lennart Schneider, et al. ∙

Bernd Bischl

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