Martin Wistuba

80 papers A* 15A 2B 8Misc 1Journal 33Unranked 20

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Hyperband-based Bayesian Optimization for Black-box Prompt Selection. ICML Lennart Schneider, Martin Wistuba, Aaron Klein, Jacek Golebiowski, Giovanni Zappella, Felice Antonio Merra
2025	J	`jnl`	SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents. CoRR Muhammad Shihab Rashid, Christian Bock, Yuan Zhuang, Alexander Buchholz, Timothy B. Esler, Simon Valentin, Luca Franceschi, Martin Wistuba, Prabhu Teja Sivaprasad, Woo Jung Kim, Anoop Deoras, Giovanni Zappella, Laurent Callot
2024	J	`jnl`	Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need. CoRR Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella
2024	J	`jnl`	Hyperband-based Bayesian Optimization for Black-box Prompt Selection. CoRR Lennart Schneider, Martin Wistuba, Aaron Klein, Jacek Golebiowski, Giovanni Zappella, Felice Antonio Merra
2023	J	`jnl`	Continual Learning with Low Rank Adaptation. CoRR Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella
2023	J	`jnl`	Deep Power Laws for Hyperparameter Optimization. CoRR Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
2023	A*	`conf`	PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
2023	J	`jnl`	Renate: A Library for Real-World Continual Learning. CoRR Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella
2023	A*	`conf`	Scaling Laws for Hyperparameter Optimization. NeurIPS Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
2023	A	`conf`	Variational Boosted Soft Trees. AISTATS Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov
2023	J	`jnl`	Variational Boosted Soft Trees. CoRR Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov
2022	A*	`conf`	Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. AAAI Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea
2022	—	`conf`	Continual Learning with Transformers for Image Classification. CVPR Workshops Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
2022	J	`jnl`	Continual Learning with Transformers for Image Classification. CoRR Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
2022	J	`jnl`	Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. CoRR Martin Wistuba, Arlind Kadra, Josif Grabocka
2022	J	`jnl`	Memory Efficient Continual Learning for Neural Text Classification. CoRR Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau
2022	A*	`conf`	Memory Efficient Continual Learning with Transformers. NeurIPS Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau
2022	A*	`conf`	Supervising the Multi-Fidelity Race of Hyperparameter Configurations. NeurIPS Martin Wistuba, Arlind Kadra, Josif Grabocka
2022	—	`conf`	Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
2021	J	`jnl`	A Comprehensive Survey on Hardware-Aware Neural Architecture Search. CoRR Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang
2021	A*	`conf`	AutoText: An End-to-End AutoAI Framework for Text. AAAI 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, Tejaswini Pedapati, Horst Samulowitz, Martin Wistuba, Yunyao Li
2021	A*	`conf`	Automated Data Science for Relational Data. ICDE Hoang Thanh Lam, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, Horst Samulowitz
2021	A*	`conf`	Few-Shot Bayesian Optimization with Deep Kernel Surrogates. ICLR Martin Wistuba, Josif Grabocka
2021	J	`jnl`	Few-Shot Bayesian Optimization with Deep Kernel Surrogates. CoRR Martin Wistuba, Josif Grabocka
2021	—	`conf`	HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. NeurIPS Datasets and Benchmarks Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
2021	J	`jnl`	HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. CoRR Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
2021	A*	`conf`	Hardware-Aware Neural Architecture Search: Survey and Taxonomy. IJCAI Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang
2021	A*	`conf`	Searching for Machine Learning Pipelines Using a Context-Free Grammar. AAAI Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea
2020	B	`conf`	Automation of Deep Learning - Theory and Practice. ICMR Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
2020	A*	`conf`	Learning to Rank Learning Curves. ICML Martin Wistuba, Tejaswini Pedapati
2020	J	`jnl`	Learning to Rank Learning Curves. CoRR Martin Wistuba, Tejaswini Pedapati
2020	B	`conf`	Survey on Automated End-to-End Data Science? IJCNN Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray
2020	—	`conf`	XferNAS: Transfer Neural Architecture Search. ECML/PKDD (3) Martin Wistuba
2019	J	`jnl`	A Survey on Neural Architecture Search. CoRR Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
2019	J	`jnl`	How can AI Automate End-to-End Data Science? CoRR Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray
2019	J	`jnl`	Inductive Transfer for Neural Architecture Optimization. CoRR Martin Wistuba, Tejaswini Pedapati
2019	J	`jnl`	NeuNetS: An Automated Synthesis Engine for Neural Network Design. CoRR 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, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir R. Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati
2019	J	`jnl`	Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit. CoRR Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba
2019	A*	`conf`	Optimal Exploitation of Clustering and History Information in Multi-armed Bandit. IJCAI Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba
2019	—	`conf`	Scalable Large Margin Gaussian Process Classification. ECML/PKDD (2) Martin Wistuba, Ambrish Rawat
2019	J	`jnl`	XferNAS: Transfer Neural Architecture Search. CoRR Martin Wistuba
2018	J	`jnl`	Adversarial Robustness Toolbox v0.2.2. CoRR Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards
2018	J	`jnl`	Automated Image Data Preprocessing with Deep Reinforcement Learning. CoRR Tran Ngoc Minh, Mathieu Sinn, Hoang Thanh Lam, Martin Wistuba
2018	—	`—`	Automated Machine Learning - Bayesian Optimization, Meta-Learning & Applications. Martin Wistuba
2018	—	`conf`	Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations. ECML/PKDD (2) Martin Wistuba
2018	J	`jnl`	Learning Features For Relational Data. CoRR Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn, Beat Buesser, Martin Wistuba
2018	B	`conf`	Practical Deep Learning Architecture Optimization. DSAA Martin Wistuba
2018	J	`jnl`	Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. Mach. Learn. Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2018	J	`jnl`	Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. CoRR Martin Wistuba, Ambrish Rawat
2017	J	`jnl`	Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. CoRR Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae
2017	A	`conf`	Automatic Frankensteining: Creating Complex Ensembles Autonomously. SDM Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2017	—	`conf`	Bayesian Optimization Combined with Successive Halving for Neural Network Architecture Optimization. AutoML@PKDD/ECML Martin Wistuba
2017	J	`jnl`	Finding Competitive Network Architectures Within a Day Using UCT. CoRR Martin Wistuba
2017	—	`conf`	Multi-Plant Photovoltaic Energy Forecasting Challenge with Regression Tree Ensembles and Hourly Average Forecasts. DC@PKDD/ECML Kathrin Bujna, Martin Wistuba
2017	—	`conf`	Personalized Deep Learning for Tag Recommendation. PAKDD (1) Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme
2017	—	`conf`	Personalized Tag Recommendation for Images Using Deep Transfer Learning. ECML/PKDD (2) Hanh T. H. Nguyen, Martin Wistuba, Lars Schmidt-Thieme
2016	J	`jnl`	Bank Card Usage Prediction Exploiting Geolocation Information. CoRR Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme
2016	J	`jnl`	Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2016	B	`conf`	Hyperparameter Optimization Machines. DSAA Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2016	—	`conf`	Learning DTW-Shapelets for Time-Series Classification. CODS Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
2016	—	`conf`	Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. ECML/PKDD (1) Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
2016	—	`conf`	Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. ECML/PKDD (1) Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2015	—	`conf`	Geo_ML @ MediaEval Placing Task 2015. MediaEval Nghia Duong-Trung, Martin Wistuba, Lucas Rêgo Drumond, Lars Schmidt-Thieme
2015	—	`conf`	Hyperparameter Optimization with Factorized Multilayer Perceptrons. ECML/PKDD (2) Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
2015	—	`conf`	Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. ECML/PKDD (2) Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2015	B	`conf`	Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. ICTAI Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme
2015	—	`conf`	Learning Data Set Similarities for Hyperparameter Optimization Initializations. MetaSel@PKDD/ECML Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2015	B	`conf`	Learning hyperparameter optimization initializations. DSAA Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2015	J	`jnl`	Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. IEEE Trans. Knowl. Data Eng. Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2015	J	`jnl`	Scalable Discovery of Time-Series Shapelets. CoRR Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2015	A*	`conf`	Sequential Model-Free Hyperparameter Tuning. ICDM Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
2015	J	`jnl`	Ultra-Fast Shapelets for Time Series Classification. CoRR Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme
2014	A*	`conf`	Learning time-series shapelets. KDD Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
2014	B	`conf`	Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. ICALT Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutiérrez Santos
2013	B	`conf`	Factorized Decision Trees for Active Learning in Recommender Systems. ICTAI Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme
2013	Misc	`conf`	Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. KI Martin Wistuba, Lars Schmidt-Thieme
2013	—	`conf`	Supervised Clustering of Social Media Streams. MediaEval Martin Wistuba, Lars Schmidt-Thieme
2013	J	`jnl`	Time-Series Classification Through Histograms of Symbolic Polynomials. CoRR Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2012	—	`conf`	Comparison of Bayesian move prediction systems for Computer Go. CIG Martin Wistuba, Lars Schaefers, Marco Platzner
2011	—	`conf`	PeerGame: Ein Peer-to-Peer-basiertes Multiplayer-Echtzeit-Strategiespiel. Informatiktage André Diekwisch, Andreas Martens, Boris Wolf, Claudia Schumacher, Claudius Jähn, Daniel Mex, David Maicher, Dominik Leibenger, Gennadij Liske, Jürgen Tessmann, Kathrin Bujna, Martin Wistuba, Matthias Fischer, Matthias Strotmeier, Peter Mahlmann, Philipp Brandes