World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
10242
World Ranking
7465
National Ranking
366

Overview

Bernd Bischl is affiliated with the Ludwig-Maximilians-Universität München in Germany. Their research contributions predominantly lie within the field of Computer Science, with a specialized focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Statistics and Probability, and Radiology, Nuclear Medicine and Imaging.

The scientist has published extensively, with a total output of over 300 papers in Computer Science and more than 200 in Artificial Intelligence alone. The main research topics in their work include Machine Learning and Data Classification, Explainable Artificial Intelligence (XAI), Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Statistical Methods and Inference, Adversarial Robustness in Machine Learning, and Domain Adaptation and Few-Shot Learning.

Frequent co-authors collaborating with Bernd Bischl include David Rügamer, Giuseppe Casalicchio, Florian Pfisterer, Mina Rezaei, and Lennart Schneider.

Common publication venues for Bernd Bischl's research are:

  • arXiv (Cornell University)
  • Data Mining and Knowledge Discovery
  • Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • Lecture Notes in Computer Science
  • Communications in Computer and Information Science

Among recent papers authored or coauthored by Bernd Bischl are:

  • Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2023), published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • Predicting personality from patterns of behavior collected with smartphones (2020), published in Proceedings of the National Academy of Sciences
  • Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features (2022), published in Computational Statistics
  • mlr3proba: an R package for machine learning in survival analysis (2021), published in Bioinformatics
  • Deep learning for survival analysis: a review (2024), published in Artificial Intelligence Review

Best Publications

  • OpenML: networked science in machine learning

    Joaquin Vanschoren;Jan N. van Rijn;Bernd Bischl;Luis Torgo

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

    Bernd Bischl;Martin Binder;Michel Lang;Tobias Pielok

  • Benchmark for filter methods for feature selection in high-dimensional classification data

    Andrea Bommert;Xudong Sun;Bernd Bischl;Jörg Rahnenführer

  • mlr: machine learning in R

    Bernd Bischl;Michel Lang;Lars Kotthoff;Julia Schiffner

  • Tunability: Importance of Hyperparameters of Machine Learning Algorithms

    Philipp Probst;Bernd Bischl;Anne-Laure Boulesteix

  • mlr3: A modern object-oriented machine learning framework in R

    Michel Lang;Martin Binder;Jakob Richter;Patrick Schratz

  • Exploratory landscape analysis

    Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss

  • Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges

    Christoph Molnar;Giuseppe Casalicchio;Bernd Bischl

  • Predicting personality from patterns of behavior collected with smartphones.

    Clemens Stachl;Quay Au;Ramona Schoedel;Samuel D Gosling;Samuel D Gosling

  • ASlib: A Benchmark Library for Algorithm Selection

    Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer

  • Tunability: Importance of Hyperparameters of Machine Learning Algorithms

    Philipp Probst;Anne-Laure Boulesteix;Bernd Bischl

  • Resampling methods for meta-model validation with recommendations for evolutionary computation

    B. Bischl;O. Mersmann;H. Trautmann;C. Weihs

  • Multi-Objective Counterfactual Explanations

    Susanne Dandl;Christoph Molnar;Martin Binder;Bernd Bischl

  • Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

    Bernd Bischl;Olaf Mersmann;Heike Trautmann;Mike Preuß

  • mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

    Bernd Bischl;Jakob Richter;Jakob Bossek;Daniel Horn

  • Visualizing the Feature Importance for Black Box Models

    Giuseppe Casalicchio;Christoph Molnar;Bernd Bischl

  • An Open Source AutoML Benchmark

    Pieter Gijsbers;Erin LeDell;Janek Thomas;Sébastien Poirier

  • OpenML: A collaborative science platform

    Jan van Rijn;Bernd Bischl;Luis Torgo;Bo Gao

  • Effectiveness of Random Search in SVM hyper-parameter tuning

    Rafael G. Mantovani;Andre L. D. Rossi;Joaquin Vanschoren;Bernd Bischl

  • General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

    Christoph Molnar;Gunnar König;Julia Herbinger;Timo Freiesleben

  • Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features.

    Florian Pargent;Florian Pfisterer;Janek Thomas;Bernd Bischl

  • mlr3proba: An R Package for Machine Learning in Survival Analysis.

    Raphael Sonabend;Franz J Király;Andreas Bender;Bernd Bischl

  • batchtools: Tools for R to work on batch systems

    Michel Lang;Bernd Bischl;Dirk Surmann

  • A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem

    Olaf Mersmann;Bernd Bischl;Heike Trautmann;Markus Wagner

  • Robust Anomaly Detection in Images Using Adversarial Autoencoders

    Laura Beggel;Michael Pfeiffer;Bernd Bischl

Frequent Co-Authors

Joaquin Vanschoren
Joaquin Vanschoren Eindhoven University of Technology
Heike Trautmann
Heike Trautmann University of Münster
Markus Bühner
Markus Bühner Ludwig-Maximilians-Universität München
Heinrich Hussmann
Heinrich Hussmann Ludwig-Maximilians-Universität München
Frank Hutter
Frank Hutter University of Freiburg
Günter Rudolph
Günter Rudolph TU Dortmund University
Frank Neumann
Frank Neumann University of Adelaide
Anne-Laure Boulesteix
Anne-Laure Boulesteix Ludwig-Maximilians-Universität München
Markus Wagner
Markus Wagner Monash University
Peter Marwedel
Peter Marwedel TU Dortmund University

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