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2020 – today
- 2024
[i28]Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella:
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need. CoRR abs/2406.03216 (2024)- 2023
- [c45]Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov:
Variational Boosted Soft Trees. AISTATS 2023: 5787-5801 - [c44]Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella:
PASHA: Efficient HPO and NAS with Progressive Resource Allocation. ICLR 2023 - [c43]Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka:
Scaling Laws for Hyperparameter Optimization. NeurIPS 2023 - [i27]Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka:
Deep Power Laws for Hyperparameter Optimization. CoRR abs/2302.00441 (2023) - [i26]Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov:
Variational Boosted Soft Trees. CoRR abs/2302.10706 (2023) - [i25]Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella:
Renate: A Library for Real-World Continual Learning. CoRR abs/2304.12067 (2023) - [i24]Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles, Giovanni Zappella:
Continual Learning with Low Rank Adaptation. CoRR abs/2311.17601 (2023) - 2022
- [c42]Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea:
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. AAAI 2022: 10228-10237 - [c41]David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau:
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML 2022: 16/1-23 - [c40]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Continual Learning with Transformers for Image Classification. CVPR Workshops 2022: 3773-3780 - [c39]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Memory Efficient Continual Learning with Transformers. NeurIPS 2022 - [c38]Martin Wistuba, Arlind Kadra, Josif Grabocka:
Supervising the Multi-Fidelity Race of Hyperparameter Configurations. NeurIPS 2022 - [i23]Martin Wistuba, Arlind Kadra, Josif Grabocka:
Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. CoRR abs/2202.09774 (2022) - [i22]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Cédric Archambeau:
Memory Efficient Continual Learning for Neural Text Classification. CoRR abs/2203.04640 (2022) - [i21]Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, Cédric Archambeau:
Continual Learning with Transformers for Image Classification. CoRR abs/2206.14085 (2022) - 2021
- [c37]Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea:
Searching for Machine Learning Pipelines Using a Context-Free Grammar. AAAI 2021: 8902-8911 - [c36]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:
AutoText: An End-to-End AutoAI Framework for Text. AAAI 2021: 16001-16003 - [c35]Hoang Thanh Lam, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, Horst Samulowitz:
Automated Data Science for Relational Data. ICDE 2021: 2689-2692 - [c34]Martin Wistuba, Josif Grabocka:
Few-Shot Bayesian Optimization with Deep Kernel Surrogates. ICLR 2021 - [c33]Hadjer Benmeziane
, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang:
Hardware-Aware Neural Architecture Search: Survey and Taxonomy. IJCAI 2021: 4322-4329 - [c32]Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka:
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. NeurIPS Datasets and Benchmarks 2021 - [i20]Martin Wistuba, Josif Grabocka:
Few-Shot Bayesian Optimization with Deep Kernel Surrogates. CoRR abs/2101.07667 (2021) - [i19]Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smaïl Niar, Martin Wistuba, Naigang Wang:
A Comprehensive Survey on Hardware-Aware Neural Architecture Search. CoRR abs/2101.09336 (2021) - [i18]Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka:
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. CoRR abs/2106.06257 (2021) - 2020
- [c31]Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. ICML 2020: 10303-10312 - [c30]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:
Survey on Automated End-to-End Data Science? IJCNN 2020: 1-9 - [c29]Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
Automation of Deep Learning - Theory and Practice. ICMR 2020: 5-6 - [c28]Martin Wistuba:
XferNAS: Transfer Neural Architecture Search. ECML/PKDD (3) 2020: 247-262 - [i17]Martin Wistuba, Tejaswini Pedapati:
Learning to Rank Learning Curves. CoRR abs/2006.03361 (2020)
2010 – 2019
- 2019
- [c27]Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba:
Optimal Exploitation of Clustering and History Information in Multi-armed Bandit. IJCAI 2019: 2016-2022 - [c26]Martin Wistuba, Ambrish Rawat:
Scalable Large Margin Gaussian Process Classification. ECML/PKDD (2) 2019: 501-516 - [i16]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:
NeuNetS: An Automated Synthesis Engine for Neural Network Design. CoRR abs/1901.06261 (2019) - [i15]Martin Wistuba, Tejaswini Pedapati:
Inductive Transfer for Neural Architecture Optimization. CoRR abs/1903.03536 (2019) - [i14]Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
A Survey on Neural Architecture Search. CoRR abs/1905.01392 (2019) - [i13]Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba:
Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit. CoRR abs/1906.03979 (2019) - [i12]Martin Wistuba:
XferNAS: Transfer Neural Architecture Search. CoRR abs/1907.08307 (2019) - [i11]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:
How can AI Automate End-to-End Data Science? CoRR abs/1910.14436 (2019) - 2018
- [b1]Martin Wistuba:
Automated Machine Learning - Bayesian Optimization, Meta-Learning & Applications. University of Hildesheim, Germany, 2018 - [j3]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. Mach. Learn. 107(1): 43-78 (2018) - [c25]Martin Wistuba:
Practical Deep Learning Architecture Optimization. DSAA 2018: 263-272 - [c24]Martin Wistuba:
Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations. ECML/PKDD (2) 2018: 243-258 - [i10]Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn, Beat Buesser, Martin Wistuba:
Learning Features For Relational Data. CoRR abs/1801.05372 (2018) - [i9]Martin Wistuba, Ambrish Rawat:
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. CoRR abs/1806.02659 (2018) - [i8]Tran Ngoc Minh, Mathieu Sinn, Hoang Thanh Lam, Martin Wistuba:
Automated Image Data Preprocessing with Deep Reinforcement Learning. CoRR abs/1806.05886 (2018) - [i7]Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards:
Adversarial Robustness Toolbox v0.2.2. CoRR abs/1807.01069 (2018) - 2017
- [c23]Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme:
Personalized Deep Learning for Tag Recommendation. PAKDD (1) 2017: 186-197 - [c22]Kathrin Bujna, Martin Wistuba:
Multi-Plant Photovoltaic Energy Forecasting Challenge with Regression Tree Ensembles and Hourly Average Forecasts. DC@PKDD/ECML 2017 - [c21]Martin Wistuba:
Bayesian Optimization Combined with Successive Halving for Neural Network Architecture Optimization. AutoML@PKDD/ECML 2017: 2-11 - [c20]Hanh T. H. Nguyen, Martin Wistuba, Lars Schmidt-Thieme:
Personalized Tag Recommendation for Images Using Deep Transfer Learning. ECML/PKDD (2) 2017: 705-720 - [c19]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Automatic Frankensteining: Creating Complex Ensembles Autonomously. SDM 2017: 741-749 - [i6]Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae:
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. CoRR abs/1711.08244 (2017) - [i5]Martin Wistuba:
Finding Competitive Network Architectures Within a Day Using UCT. CoRR abs/1712.07420 (2017) - 2016
- [j2]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. 49(2): 429-454 (2016) - [c18]Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Learning DTW-Shapelets for Time-Series Classification. CODS 2016: 3:1-3:8 - [c17]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Hyperparameter Optimization Machines. DSAA 2016: 41-50 - [c16]Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. ECML/PKDD (1) 2016: 33-48 - [c15]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. ECML/PKDD (1) 2016: 199-214 - [i4]Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Bank Card Usage Prediction Exploiting Geolocation Information. CoRR abs/1610.03996 (2016) - 2015
- [j1]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. IEEE Trans. Knowl. Data Eng. 27(6): 1683-1695 (2015) - [c14]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Learning hyperparameter optimization initializations. DSAA 2015: 1-10 - [c13]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Sequential Model-Free Hyperparameter Tuning. ICDM 2015: 1033-1038 - [c12]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. ICTAI 2015: 72-79 - [c11]Nghia Duong-Trung, Martin Wistuba, Lucas Rêgo Drumond, Lars Schmidt-Thieme:
Geo_ML @ MediaEval Placing Task 2015. MediaEval 2015 - [c10]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Learning Data Set Similarities for Hyperparameter Optimization Initializations. MetaSel@PKDD/ECML 2015: 15-26 - [c9]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Hyperparameter Optimization with Factorized Multilayer Perceptrons. ECML/PKDD (2) 2015: 87-103 - [c8]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. ECML/PKDD (2) 2015: 104-119 - [i3]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Discovery of Time-Series Shapelets. CoRR abs/1503.03238 (2015) - [i2]Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme:
Ultra-Fast Shapelets for Time Series Classification. CoRR abs/1503.05018 (2015) - 2014
- [c7]Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutiérrez Santos:
Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems. ICALT 2014: 746-748 - [c6]Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Learning time-series shapelets. KDD 2014: 392-401 - 2013
- [c5]Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Factorized Decision Trees for Active Learning in Recommender Systems. ICTAI 2013: 404-411 - [c4]Martin Wistuba, Lars Schmidt-Thieme:
Move Prediction in Go - Modelling Feature Interactions Using Latent Factors. KI 2013: 260-271 - [c3]Martin Wistuba, Lars Schmidt-Thieme:
Supervised Clustering of Social Media Streams. MediaEval 2013 - [i1]Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme:
Time-Series Classification Through Histograms of Symbolic Polynomials. CoRR abs/1307.6365 (2013) - 2012
- [c2]Martin Wistuba, Lars Schaefers, Marco Platzner:
Comparison of Bayesian move prediction systems for Computer Go. CIG 2012: 91-99 - 2011
- [c1]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:
PeerGame: Ein Peer-to-Peer-basiertes Multiplayer-Echtzeit-Strategiespiel. Informatiktage 2011: 203-206
Coauthor Index
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