Martin Wistuba

According to our database, Martin Wistuba authored at least 70 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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

2023

Continual Learning with Low Rank Adaptation.

Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella

CoRR, 2023

Renate: A Library for Real-World Continual Learning.

Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella

CoRR, 2023

Deep Power Laws for Hyperparameter Optimization.

Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka

CoRR, 2023

Scaling Laws for Hyperparameter Optimization.

Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PASHA: Efficient HPO and NAS with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Variational Boosted Soft Trees.

Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Memory Efficient Continual Learning for Neural Text Classification.

Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau

CoRR, 2022

Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning.

Martin Wistuba, Arlind Kadra, Josif Grabocka

CoRR, 2022

Supervising the Multi-Fidelity Race of Hyperparameter Configurations.

Martin Wistuba, Arlind Kadra, Josif Grabocka

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Memory Efficient Continual Learning with Transformers.

Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Continual Learning with Transformers for Image Classification.

Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 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

Proceedings of the International Conference on Automated Machine Learning, 2022

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization.

Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

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

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML.

Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Hardware-Aware Neural Architecture Search: Survey and Taxonomy.

Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Few-Shot Bayesian Optimization with Deep Kernel Surrogates.

Martin Wistuba, Josif Grabocka

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 37th IEEE International Conference on Data Engineering, 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, Tejaswini Pedapati, Horst Samulowitz, Martin Wistuba, Yunyao Li

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Searching for Machine Learning Pipelines Using a Context-Free Grammar.

Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

XferNAS: Transfer Neural Architecture Search.

Martin Wistuba

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Automation of Deep Learning - Theory and Practice.

Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

Proceedings of the 2020 on International Conference on Multimedia Retrieval, 2020

Survey on Automated End-to-End Data Science?

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

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Learning to Rank Learning Curves.

Martin Wistuba, Tejaswini Pedapati

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

How can AI Automate End-to-End Data Science?

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

CoRR, 2019

A Survey on Neural Architecture Search.

Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

CoRR, 2019

Inductive Transfer for Neural Architecture Optimization.

Martin Wistuba, Tejaswini Pedapati

CoRR, 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, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir R. Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati

CoRR, 2019

Scalable Large Margin Gaussian Process Classification.

Martin Wistuba, Ambrish Rawat

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Optimal Exploitation of Clustering and History Information in Multi-armed Bandit.

Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Automated Machine Learning - Bayesian Optimization, Meta-Learning & Applications.

Martin Wistuba

PhD thesis, 2018

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Mach. Learn., 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

Automated Image Data Preprocessing with Deep Reinforcement Learning.

Tran Ngoc Minh, Mathieu Sinn, Hoang Thanh Lam, Martin Wistuba

CoRR, 2018

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data.

Martin Wistuba, Ambrish Rawat

CoRR, 2018

Learning Features For Relational Data.

Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn, Beat Buesser, Martin Wistuba

CoRR, 2018

Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations.

Martin Wistuba

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Practical Deep Learning Architecture Optimization.

Martin Wistuba

Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics, 2018

2017

Finding Competitive Network Architectures Within a Day Using UCT.

Martin Wistuba

CoRR, 2017

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning.

Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae

CoRR, 2017

Automatic Frankensteining: Creating Complex Ensembles Autonomously.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Bayesian Optimization Combined with Successive Halving for Neural Network Architecture Optimization.

Martin Wistuba

Proceedings of the International Workshop on Automatic Selection, 2017

Personalized Tag Recommendation for Images Using Deep Transfer Learning.

Hanh T. H. Nguyen, Martin Wistuba, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Multi-Plant Photovoltaic Energy Forecasting Challenge with Regression Tree Ensembles and Hourly Average Forecasts.

Kathrin Bujna, Martin Wistuba

Proceedings of the ECML/PKDD Discovery Challenges co-located with European Conference on Machine Learning, 2017

Personalized Deep Learning for Tag Recommendation.

Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2017

2016

Fast classification of univariate and multivariate time series through shapelet discovery.

Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme

Knowl. Inf. Syst., 2016

Bank Card Usage Prediction Exploiting Geolocation Information.

Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme

CoRR, 2016

Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Scalable Hyperparameter Optimization with Products of Gaussian Process Experts.

Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Hyperparameter Optimization Machines.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Learning DTW-Shapelets for Time-Series Classification.

Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme

Proceedings of the 3rd IKDD Conference on Data Science, 2016

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

Ultra-Fast Shapelets for Time Series Classification.

Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme

CoRR, 2015

Scalable Discovery of Time-Series Shapelets.

Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme

CoRR, 2015

Learning Data Set Similarities for Hyperparameter Optimization Initializations.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), 2015

Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Hyperparameter Optimization with Factorized Multilayer Perceptrons.

Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Geo_ML @ MediaEval Placing Task 2015.

Nghia Duong-Trung, Martin Wistuba, Lucas Rêgo Drumond, Lars Schmidt-Thieme

Proceedings of the Working Notes Proceedings of the MediaEval 2015 Workshop, 2015

Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons.

Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme

Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence, 2015

Sequential Model-Free Hyperparameter Tuning.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Learning hyperparameter optimization initializations.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

2014

Learning time-series shapelets.

Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems.

Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutiérrez Santos

Proceedings of the IEEE 14th International Conference on Advanced Learning Technologies, 2014

2013

Time-Series Classification Through Histograms of Symbolic Polynomials.

Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme

CoRR, 2013

Supervised Clustering of Social Media Streams.

Martin Wistuba, Lars Schmidt-Thieme

Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, 2013

Move Prediction in Go - Modelling Feature Interactions Using Latent Factors.

Martin Wistuba, Lars Schmidt-Thieme

Proceedings of the KI 2013: Advances in Artificial Intelligence, 2013

Factorized Decision Trees for Active Learning in Recommender Systems.

Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme

Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence, 2013

2012

Comparison of Bayesian move prediction systems for Computer Go.

Martin Wistuba, Lars Schaefers, Marco Platzner

Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games, 2012

2011

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, Matthias Fischer, Matthias Strotmeier, Peter Mahlmann, Philipp Brandes

Proceedings of the Informatiktage 2011, 2011