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09/05/2023
Probabilistic Self-supervised Learning via Scoring Rules Minimization
In this paper, we propose a novel probabilistic self-supervised learning...
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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...
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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...
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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...
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07/14/2023
How Different Is Stereotypical Bias Across Languages?
Recent studies have demonstrated how to assess the stereotypical bias in...
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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...
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06/16/2023
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers
Deep active learning (DAL) seeks to reduce annotation costs by enabling ...
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06/01/2023
Decomposing Global Feature Effects Based on Feature Interactions
Global feature effect methods, such as partial dependence plots, provide...
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05/25/2023
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to ...
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05/04/2023
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
This work introduces interpretable regional descriptors, or IRDs, for lo...
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04/13/2023
counterfactuals: An R Package for Counterfactual Explanation Methods
Counterfactual explanation methods provide information on how feature va...
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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...
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03/20/2023
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
While recent advances in large-scale foundational models show promising ...
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03/15/2023
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques t...
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12/08/2022
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Modern machine learning models are often constructed taking into account...
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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...
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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...
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09/14/2022
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Contrastive learning is among the most successful methods for visual rep...
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09/14/2022
Improved proteasomal cleavage prediction with positive-unlabeled learning
Accurate in silico modeling of the antigen processing pathway is crucial...
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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...
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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...
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07/30/2022
Tackling Neural Architecture Search With Quality Diversity Optimization
Neural architecture search (NAS) has been studied extensively and has gr...
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07/25/2022
AMLB: an AutoML Benchmark
Comparing different AutoML frameworks is notoriously challenging and oft...
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06/15/2022
Multi-Objective Hyperparameter Optimization – An Overview
Hyperparameter optimization constitutes a large part of typical modern m...
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06/11/2022
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution
Despite all the benefits of automated hyperparameter optimization (HPO),...
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05/25/2022
Factorized Structured Regression for Large-Scale Varying Coefficient Models
Recommender Systems (RS) pervade many aspects of our everyday digital li...
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05/19/2022
What Is Fairness? Implications For FairML
A growing body of literature in fairness-aware ML (fairML) aspires to mi...
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05/11/2022
Efficient Automated Deep Learning for Time Series Forecasting
Recent years have witnessed tremendously improved efficiency of Automate...
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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...
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03/21/2022
Distributed non-disclosive validation of predictive models by a modified ROC-GLM
Distributed statistical analyses provide a promising approach for privac...
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02/16/2022
Cross-Modal Common Representation Learning with Triplet Loss Functions
Common representation learning (CRL) learns a shared embedding between t...
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02/15/2022
REPID: Regional Effect Plots with implicit Interaction Detection
Machine learning models can automatically learn complex relationships, s...
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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...
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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 ...
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01/31/2022
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection
Pseudo-labeling solutions for positive-unlabeled (PU) learning have the ...
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01/21/2022
Marginal Effects for Non-Linear Prediction Functions
Beta coefficients for linear regression models represent the ideal form ...
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11/29/2021
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Automated hyperparameter optimization (HPO) has gained great popularity ...
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11/08/2021
Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to...
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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...
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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...
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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...
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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...
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09/15/2021
Deep Bregman Divergence for Contrastive Learning of Visual Representations
Deep Bregman divergence measures divergence of data points using neural ...
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09/12/2021
Automatic Componentwise Boosting: An Interpretable AutoML System
In practice, machine learning (ML) workflows require various different s...
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09/11/2021
Learning Statistical Representation with Joint Deep Embedded Clustering
One of the most promising approaches for unsupervised learning is combin...
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09/08/2021
YAHPO Gym – Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization
When developing and analyzing new hyperparameter optimization (HPO) meth...
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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...
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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...
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07/13/2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperp...
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07/04/2021