← Authors

Bernd Bischl

32 papers · 2016–2025 · 11 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🌍 Conference Polyglot (11) πŸƒ Academic Marathon (9) 🐝 Cross-Pollinator (8) 🌈 Renaissance Researcher (7) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (5) πŸ—ƒοΈ Keyword Collector (101) ❓ The Questioner (2) πŸ’Ž Century Club (32) ⚑ Prolific Year (6)

Conferences

AUTOML (5) ICLR (5) JMLR (5) ICML (4) NIPS (3) ACL (2) AISTATS (2) EMNLP (2) WACV (2) IJCAI (1) UAI (1)

Top co-authors

David Rügamer (8) Giuseppe Casalicchio (6) Lennart Schneider (6) Lisa Wimmer (4) Matthias Feurer (4) Mina Rezaei (4) Eyke Hüllermeier (3) Matthias Aßenmacher (3) Florian Pfisterer (3) Marius Lindauer (3)

Keywords

hyperparameter optimization (6) uncertainty quantification (3) surrogate model (3) hyperparameter tuning (2) feature interaction (2) epistemic uncertainty (2) text classification (2) partial dependence plot (2) quality diversity (2) model selection (2) bayesian optimization (2) multi-objective optimization (2) automated machine learning (2) educational resources (1) benchmark suite (1) probabilistic modeling (1) benchmark evaluation (1) deep clustering (1) feature selection (1) handwriting recognition (1)

Papers

Revisiting Active Learning under (Human) Label Variation EMNLP 2025
Overtuning in Hyperparameter Optimization AUTOML 2025
Revisiting Unbiased Implicit Variational Inference ICML 2025
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries ICLR 2025
Calibrating LLMs with Information-Theoretic Evidential Deep Learning ICLR 2025
Efficient and Accurate Explanation Estimation with Distribution Compression ICLR 2025
Constrained Probabilistic Mask Learning for Task-Specific Undersampled MRI Reconstruction WACV 2024
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization NIPS 2024
Collaborative Development of Modular Open Source Educational Resources for Natural Language Processing ACL 2024
Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization ICLR 2024
Position: Why We Must Rethink Empirical Research in Machine Learning ICML 2024
Position: A Call to Action for a Human-Centered AutoML Paradigm ICML 2024
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? ICML 2024
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract) IJCAI 2024
AMLB: an AutoML Benchmark JMLR 2024
Decomposing Global Feature Effects Based on Feature Interactions JMLR 2024
Symbolic Explanations for Hyperparameter Optimization AUTOML 2023
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI 2023
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning ACL 2023
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks AISTATS 2023
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML AUTOML 2023
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent ICLR 2023
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation NIPS 2022
REPID: Regional Effect Plots with implicit Interaction Detection AISTATS 2022
Tackling Neural Architecture Search With Quality Diversity Optimization AUTOML 2022
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization AUTOML 2022
Joint Classification and Trajectory Regression of Online Handwriting Using a Multi-Task Learning Approach WACV 2022
CC-Top: Constrained Clustering for Dynamic Topic Discovery EMNLP 2022
mlr3pipelines - Flexible Machine Learning Pipelines in R JMLR 2021
Explaining Hyperparameter Optimization via Partial Dependence Plots NIPS 2021
Tunability: Importance of Hyperparameters of Machine Learning Algorithms JMLR 2019
mlr: Machine Learning in R JMLR 2016