PeerGame: Ein Peer-to-Peer-basiertes Multiplayer-Echtzeit-Strategiespiel. - André Diekwisch; Andreas Martens; Boris Wolf; Claudia Schumacher; Claudius Jähn; Daniel Mex; David Maicher; Dominik Leibenger; Gennadij Liske; Jürgen Tessmann; Kathrin Bujna; Martin Wistuba; et al. (Informatiktage, 2011)

Comparison of Bayesian move prediction systems for Computer Go. - Martin Wistuba; Lars Schaefers; Marco Platzner (CIG, 2012)

Factorized Decision Trees for Active Learning in Recommender Systems. - Rasoul Karimi; Martin Wistuba; Alexandros Nanopoulos; Lars Schmidt-Thieme (ICTAI, 2013)

Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. - Martin Wistuba; Lars Schmidt-Thieme (KI, 2013)

Supervised Clustering of Social Media Streams. - Martin Wistuba; Lars Schmidt-Thieme (MediaEval, 2013)

Time-Series Classification Through Histograms of Symbolic Polynomials. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (CoRR, 2013)

Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. - Carlotta Schatten; Martin Wistuba; Lars Schmidt-Thieme; Sergio Gutiérrez Santos (ICALT, 2014)

Learning time-series shapelets. - Josif Grabocka; Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (KDD, 2014)

Learning hyperparameter optimization initializations. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (DSAA, 2015)

Sequential Model-Free Hyperparameter Tuning. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (ICDM, 2015)

Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. - Nicolas Schilling; Martin Wistuba; Lucas Drumond; Lars Schmidt-Thieme (ICTAI, 2015)

Geo_ML @ MediaEval Placing Task 2015. - Nghia Duong-Trung; Martin Wistuba; Lucas Rêgo Drumond; Lars Schmidt-Thieme (MediaEval, 2015)

Hyperparameter Optimization with Factorized Multilayer Perceptrons. - Nicolas Schilling; Martin Wistuba; Lucas Drumond; Lars Schmidt-Thieme (ECML/PKDD (2), 2015)

Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (ECML/PKDD (2), 2015)

Learning Data Set Similarities for Hyperparameter Optimization Initializations. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (MetaSel@PKDD/ECML, 2015)

Scalable Discovery of Time-Series Shapelets. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (CoRR, 2015)

Ultra-Fast Shapelets for Time Series Classification. - Martin Wistuba; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2015)

Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (IEEE Trans. Knowl. Data Eng., 2015)

Learning DTW-Shapelets for Time-Series Classification. - Mit Shah; Josif Grabocka; Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (CODS, 2016)

Hyperparameter Optimization Machines. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (DSAA, 2016)

Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. - Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (ECML/PKDD (1), 2016)

Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (ECML/PKDD (1), 2016)

Bank Card Usage Prediction Exploiting Geolocation Information. - Martin Wistuba; Nghia Duong-Trung; Nicolas Schilling; Lars Schmidt-Thieme (CoRR, 2016)

Fast classification of univariate and multivariate time series through shapelet discovery. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (Knowl. Inf. Syst., 2016)

Personalized Deep Learning for Tag Recommendation. - Hanh T. H. Nguyen; Martin Wistuba; Josif Grabocka; Lucas Rêgo Drumond; Lars Schmidt-Thieme (PAKDD (1), 2017)

Multi-Plant Photovoltaic Energy Forecasting Challenge with Regression Tree Ensembles and Hourly Average Forecasts. - Kathrin Bujna; Martin Wistuba (DC@PKDD/ECML, 2017)

Personalized Tag Recommendation for Images Using Deep Transfer Learning. - Hanh T. H. Nguyen; Martin Wistuba; Lars Schmidt-Thieme (ECML/PKDD (2), 2017)

Bayesian Optimization Combined with Successive Halving for Neural Network Architecture Optimization. - Martin Wistuba (AutoML@PKDD/ECML, 2017)

Automatic Frankensteining: Creating Complex Ensembles Autonomously. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (SDM, 2017)

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. - Ambrish Rawat; Martin Wistuba; Maria-Irina Nicolae (CoRR, 2017)

Finding Competitive Network Architectures Within a Day Using UCT. - Martin Wistuba (CoRR, 2017)

Practical Deep Learning Architecture Optimization. - Martin Wistuba (DSAA, 2018)

Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations. - Martin Wistuba (ECML/PKDD (2), 2018)

Learning Features For Relational Data. - Hoang Thanh Lam; Tran Ngoc Minh; Mathieu Sinn; Beat Buesser; Martin Wistuba (CoRR, 2018)

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. - Martin Wistuba; Ambrish Rawat (CoRR, 2018)

Automated Image Data Preprocessing with Deep Reinforcement Learning. - Tran Ngoc Minh; Mathieu Sinn; Hoang Thanh Lam; Martin Wistuba (CoRR, 2018)

Adversarial Robustness Toolbox v0.2.2. - Maria-Irina Nicolae; Mathieu Sinn; Tran Ngoc Minh; Ambrish Rawat; Martin Wistuba; Valentina Zantedeschi; Ian M. Molloy; Benjamin Edwards (CoRR, 2018)

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. - Martin Wistuba; Nicolas Schilling; Lars Schmidt-Thieme (Mach. Learn., 2018)

Automated Machine Learning - Bayesian Optimization, Meta-Learning & Applications. - Martin Wistuba (2018)

Optimal Exploitation of Clustering and History Information in Multi-armed Bandit. - Djallel Bouneffouf 001; Srinivasan Parthasarathy 002; Horst Samulowitz; Martin Wistuba (IJCAI, 2019)

Scalable Large Margin Gaussian Process Classification. - Martin Wistuba; Ambrish Rawat (ECML/PKDD (2), 2019)

NeuNetS: An Automated Synthesis Engine for Neural Network Design. - Atin Sood; Benjamin Elder; Benjamin Herta; Chao Xue; Costas Bekas; A. Cristiano I. Malossi; Debashish Saha; Florian Scheidegger; Ganesh Venkataraman; Gegi Thomas; Giovanni Mariani; Hendrik Strobelt; et al. (CoRR, 2019)

Inductive Transfer for Neural Architecture Optimization. - Martin Wistuba; Tejaswini Pedapati (CoRR, 2019)

A Survey on Neural Architecture Search. - Martin Wistuba; Ambrish Rawat; Tejaswini Pedapati (CoRR, 2019)

How can AI Automate End-to-End Data Science? - Charu C. Aggarwal; Djallel Bouneffouf 001; Horst Samulowitz; Beat Buesser; Thanh Hoang; Udayan Khurana; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (CoRR, 2019)

Learning to Rank Learning Curves. - Martin Wistuba; Tejaswini Pedapati (ICML, 2020)

Survey on Automated End-to-End Data Science? - Djallel Bouneffouf 001; Charu C. Aggarwal; Thanh Hoang; Udayan Khurana; Horst Samulowitz; Beat Buesser; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (IJCNN, 2020)

Automation of Deep Learning - Theory and Practice. - Martin Wistuba; Ambrish Rawat; Tejaswini Pedapati (ICMR, 2020)

XferNAS: Transfer Neural Architecture Search. - Martin Wistuba (ECML/PKDD (3), 2020)

Searching for Machine Learning Pipelines Using a Context-Free Grammar. - Radu Marinescu 002; Akihiro Kishimoto; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea (AAAI, 2021)

AutoText: An End-to-End AutoAI Framework for Text. - Arunima Chaudhary; Alayt Issak; Kiran Kate; Yannis Katsis; Abel N. Valente; Dakuo Wang; Alexandre V. Evfimievski; Sairam Gurajada; Ban Kawas; A. Cristiano I. Malossi; Lucian Popa 001; Tejaswini Pedapati; et al. (AAAI, 2021)

Automated Data Science for Relational Data. - Hoang Thanh Lam; Beat Buesser; Hong Min; Tran Ngoc Minh; Martin Wistuba; Udayan Khurana; Gregory Bramble; Theodoros Salonidis; Dakuo Wang; Horst Samulowitz (ICDE, 2021)

Few-Shot Bayesian Optimization with Deep Kernel Surrogates. - Martin Wistuba; Josif Grabocka (ICLR, 2021)

Hardware-Aware Neural Architecture Search: Survey and Taxonomy. - Hadjer Benmeziane; Kaoutar El Maghraoui; Hamza Ouarnoughi; Smaïl Niar; Martin Wistuba; Naigang Wang (IJCAI, 2021)

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. - Sebastian Pineda-Arango; Hadi S. Jomaa; Martin Wistuba; Josif Grabocka (NeurIPS Datasets and Benchmarks, 2021)

A Comprehensive Survey on Hardware-Aware Neural Architecture Search. - Hadjer Benmeziane; Kaoutar El Maghraoui; Hamza Ouarnoughi; Smaïl Niar; Martin Wistuba; Naigang Wang (CoRR, 2021)

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. - Akihiro Kishimoto; Djallel Bouneffouf 001; Radu Marinescu 002; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea (AAAI, 2022)

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. - David Salinas; Matthias W. Seeger; Aaron Klein; Valerio Perrone; Martin Wistuba; Cédric Archambeau (AutoML, 2022)

Continual Learning with Transformers for Image Classification. - Beyza Ermis; Giovanni Zappella; Martin Wistuba; Aditya Rawal; Cédric Archambeau (CVPR Workshops, 2022)

Memory Efficient Continual Learning with Transformers. - Beyza Ermis; Giovanni Zappella; Martin Wistuba; Aditya Rawal; Cédric Archambeau (NeurIPS, 2022)

Supervising the Multi-Fidelity Race of Hyperparameter Configurations. - Martin Wistuba; Arlind Kadra; Josif Grabocka (NeurIPS, 2022)

Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. - Martin Wistuba; Arlind Kadra; Josif Grabocka (CoRR, 2022)

Memory Efficient Continual Learning for Neural Text Classification. - Beyza Ermis; Giovanni Zappella; Martin Wistuba; Cédric Archambeau (CoRR, 2022)

Variational Boosted Soft Trees. - Tristan Cinquin; Tammo Rukat; Philipp Schmidt 002; Martin Wistuba; Artur Bekasov (AISTATS, 2023)

PASHA: Efficient HPO and NAS with Progressive Resource Allocation. - Ondrej Bohdal; Lukas Balles; Martin Wistuba; Beyza Ermis; Cédric Archambeau; Giovanni Zappella (ICLR, 2023)

Scaling Laws for Hyperparameter Optimization. - Arlind Kadra; Maciej Janowski; Martin Wistuba; Josif Grabocka (NeurIPS, 2023)

Deep Power Laws for Hyperparameter Optimization. - Arlind Kadra; Maciej Janowski; Martin Wistuba; Josif Grabocka (CoRR, 2023)

Renate: A Library for Real-World Continual Learning. - Martin Wistuba; Martin Ferianc; Lukas Balles; Cédric Archambeau; Giovanni Zappella (CoRR, 2023)

Continual Learning with Low Rank Adaptation. - Martin Wistuba; Prabhu Teja Sivaprasad; Lukas Balles; Giovanni Zappella (CoRR, 2023)

Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need. - Martin Wistuba; Prabhu Teja Sivaprasad; Lukas Balles; Giovanni Zappella (CoRR, 2024)

Hyperband-based Bayesian Optimization for Black-box Prompt Selection. - Lennart Schneider; Martin Wistuba; Aaron Klein; Jacek Golebiowski; Giovanni Zappella; Felice Antonio Merra (CoRR, 2024)

SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents. - Muhammad Shihab Rashid; Christian Bock; Yuan Zhuang; Alexander Buchholz; Timothy B. Esler; Simon Valentin; Luca Franceschi 003; Martin Wistuba; Prabhu Teja Sivaprasad; Woo Jung Kim; Anoop Deoras; Giovanni Zappella; et al. (CoRR, 2025)