Bernd Bischl
32 papers · 2016–2025 · 11 conferences · across top CS/AI conferences
Achievements
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๐ Conference Polyglot (11) ๐งญ Keyword Pioneer ๐ Interdisciplinary Bridge ๐บ๏ธ Taxonomy Completionist (10) ๐ Academic Marathon (9)
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Academic Marathon
(9)
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Cross-Pollinator
(8)
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Renaissance Researcher
(7)
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Triple Crown
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Keyword Champion
(2)
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Trend Setter
๐ฅ
Unstoppable
(5)
๐๏ธ
Keyword Collector
(101)
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The Questioner
(2)
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Century Club
(32)
โก
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
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