Date | Person in charge | Paper |
Multi-objective Hyperband:
| ||
Fair AutoML Through Multi-objective Optimization: https://openreview.net/pdf?id=KwLWsm5idpR | ||
Robust Multi-Objective Bayesian Optimization Under Input Noise: https://arxiv.org/abs/2202.07549 | ||
Promoting Fairness through Hyperparameter Optimization: https://arxiv.org/abs/2103.12715 | ||
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks Sakr | ||
Wed, 14:30 29.06.2022 | Matthias | Promoting Fairness through Hyperparameter Optimization Cruz et al., ICDM 2021 |
Wed, 14:30 15.06.2022 | Matthias | Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cedric Archambeau |
Wed, 14:30 | Matthias | [2106.12639] Multi-objective Asynchronous Successive Halving Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas and Cédric Archambeau |
Wed, 15:00 18.05.2022 | Matthias | An Empirical Study of Modular Bias Mitigators and Ensembles Michael Feffer and Martin Hirzel and Samuel C. Hoffman and Kiran Kate and Parikshit Ram and Avraham Shinnar |
Wed, 10:00 12.05.2022 | Katharina | Quingyun Wu and Chi Wang (arXiv 2021) |
Wed, 15:00 04.05.2022 | Matthias | Data Cleaning and AutoML: Would an optimizer choose to clean? |
Wed, 27.04.2022 | Canceled | |
Tue, 21.12.2021, 13:00 | Katharina | Benchmarking Multimodal AutoML for Tabular Data with Text Fields Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander Smola (NeurIPS DBT’21) |
Wed, 01.12.2021 | Matthias | Continue: AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space. |
Wed, 24.11.2021, 13:00 | Matthias | OBOE: Collaborative filtering for AutoML model selection. |
Wed, 08.09.2021, 13:00 | FLAML: A Fast and Lightweight AutoML Library Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu MLSys 2021 |
2018+2019
2021 | ||
Wed, 19.05.2021, 13:00 | Matthias | Scaling tree-based automated machine learning to biomedical big data with a feature set selector Trang T. Le, Weixuan Fu and Jason H. Moore (2020). Bioinformatics.36(1): 250-256. TPOT-NN: augmenting tree-based automated machine learning with neural network estimators Joseph D. Romano, Trang T. Le, Weixuan Fu & Jason H. Moore Genetic Programming and Evolvable Machines (2021) |
Wed, 28.04.2021, 13:00 | Katharina | TPOT-SH: A Faster Optimization Algorithm to Solve the AutoML Problem on Large Datasets Laurent Parmentier; Olivier Nicol; Laetitia Jourdan; Marie-Eleonore Kessaci 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) |
Wed, 31.03.2021, 13:00 | Matthias | Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). Proceedings of GECCO 2016, pages 485-492. Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). Applications of Evolutionary Computation, pages 123-137. |
2018+2019 | ||
21.10.2019 | Arber | Scalable Global Optimization via Local Bayesian Optimization David Eriksson, Michael Pearce, Jacob R Gardner, Ryan Turner, Matthias Poloczek https://arxiv.org/pdf/1910.01739.pdf NeurIPS 2019 |
07.10.2019 | Matthias | Tunability: Importance of Hyperparameters of Machine Learning Algorithms Philipp Probst, Anne-Laure Boulesteix and Bernd Bischl http://jmlr.org/papers/volume20/18-444/18-444.pdf JMLR |
22.08.19 | Arber | Transferring Knowledge across Learning Processes Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou https://openreview.net/forum?id=HygBZnRctX Oral at ICLR 2019 |
15.08.2019 | Zheng | Neural Arithmetic Logic Units Andrew Trask et al., NeurIPS 2018 https://papers.nips.cc/paper/8027-neural-arithmetic-logic-units.html |
08.08.19 | Riccardo Perego | Safe Global Optimization in the delta-Lipschitz Framework Paper available upon request |
25.07.19 | Raghu | Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Kurtland Chua, Roberto Calandra, Rowan McAllister and Sergey Levine NeurIPS 2018 |
27.06.2019 | Lucas Zimmer | TransferNAS |
30.05.2019 | - | Christ Assencion |
09.05.2019 | Jörg | Efficient Reinforcement Learning through Evolving Neural Network Topologies Kenneth O. Stanley and Risto Miikkulainen Gecco 2002 http://nn.cs.utexas.edu/downloads/papers/stanley.gecco02_1.pdf |
02.05.2019 | André | ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning Xudong Sun, Jiali Lin, Bernd Bischl |
25.04.2019 | Arbër | Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kandasamy et al. NeurIPS 2018 |
12.04.2019 | David-Elias | Visualizing the Feature Importance for Black Box Models Giuseppe Casalicchio, Christoph Molnar and Bernd Bischl Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD 2018) https://link.springer.com/chapter/10.1007/978-3-030-10925-7_40 |
22.03.2019 | Arlind | Investigating how Deep Learning scales with dataset size M.Sc. Thesis presentation |
15.03.2019 | Kevin | Kick-off M.Sc presentation |
08.03.2019 | Theresa | Kick-off M.Sc presentation: Algorithm control across instances |
01.03.2019 15:00 | Benjamin | M.Sc. defense |
22.02.2019 | Matthias | Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates Ilievski et al., AAAI 2017 |
15.02.2019 | Arber | M.Sc. presentation: Deconstructing Differentiable Architecture Search |
07.02.2019 | Matilde | Refresher on unconstrained optimization covering minimizer characterization, general algorithms, automatic differentiation, Gauss-Newton method and the linear conjugate gradient method. Part 2 |
01.02.2019 | Matilde | Refresher on unconstrained optimization covering minimizer characterization, general algorithms, automatic differentiation, Gauss-Newton method and the linear conjugate gradient method. Part 1 |
18.01.2019 | Noor | Differential Evolution |
16.01.2019 (01:00PM) | Gresa | Master Kick-off presentation: Learning to optimize |
16.01.2019 | Michael | Master Kick-off presentation: Robust NAS across datasets |
16.01.2019 | Matthias U. | Master Kick-off presentation: warmstarting of Auto-PyTorch |
08.12.2018 | Marius | TEDx Practice talk on AutoAI |
14.11.2018 | Max, Matthias, Michael | Master Project Defense: AutoNet 2.0 |
30.11.2018 | Kilian | Bachelor Thesis Defense: Exploration Strategies for Online Algorithm Selection |
25.10.2018 | Luca Francesci | Guest Talk |
18.10.2018 | Marius | AutoLoss Paper |
24.08.2018 | Raghu | Towards AutoML in the presence of Drift: first results Madrid, Escalante, Morales, Tu, Yu, Sun-Hosoya, Guyon and Sebag |
10.08.2018 | Matthias | AutoPrognosis Alaa and Van der Schaar, In ICML 2018 |
15.06.2018 | Stefan | Multi-Information Source Optimization Poloczek, Wang and Frazier, In NIPS 2017 https://papers.nips.cc/paper/7016-multi-information-source-optimization.html |
23.05.2018 | Katharina | Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees Kleinberg, Leyton-Brown and Lucier, In IJCAI 2017 http://www.cs.ubc.ca/~kevinlb/pub.php?u=2017-StructuredProcrastination.pdf |
02.05.2018 | David-Elias Künstle | Multi-fidelity Bayesian Optimisation with Continuous Approximations kirthevasan kandasamy · Gautam Dasarathy · Barnabás Póczos · Jeff Schneider |
09.04.2018 | Jan/Matthias | Patryk Chrabaszcz: Neural Architecture Search for Recurrent Neural Networks Mohammad-Ali Arabi: Modeling meta-data using multi-variate KDEs. |
05.03.2018 | Matilde | Duchi et al. Adaptive subgradient methods for online and stochastic optimization |
26.02.2018 | Stefan | Hazan, Klivans, Yuan Hyperparameter Optimization: A spectral approach |
19.02.2018 | Matthias | Matthias Feurer, Benjamin Letham, Eytan Bakshy Scalable Meta-Learning for Bayesian Optimization |
05.02.2018 | Moritz Freidank | Levy, Hoffman and Sohl-Dickstein Generalizing Hamiltonian Monte Carlo with Neural Networks |
30.01.2018 | Andre, Frank, Aaron, Marius, Jan | Bayesian optimization & Meta-learning workshop wrap-up part 2 |
22.01.2018 | Matthias, Katharina, Stefan, Matilde | Bayesian optimization & Meta-learning workshop wrap-up part 1: |
2017 | ||
Date | Person in charge | Paper |
18/12/2017 | Jan | Benjamin Strang thesis defense |
27/11/2017 | Frank | Patryk thesis defense |
20/11/2017 | Marius | Michael Rudolph’s Master thesis defense on “Black-Box Optimization of Large-Scale Networks of Distributed Energy Systems” |
13/11/2017 | Ilya/Frank | Muneeb thesis defense |
06/11/2017 | Matthias | Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Elliot Karro, D. Sculley Google Vizier: A Service for Black-Box Optimization https://research.google.com/pubs/pub46180.html |
23/08/2017 | Katharina | 20+20 presentation on current research |
16/08/2017 | - | No Reading Group |
09/08/2017 | - | No Reading Group |
02/08/2017 | Student(s) | Lukas Gemein: Automated EEG Diagnosis Christina Hernández Wunsch: Recurrent Neural Networks for Learning Curves |
26/07/2017 | - | No Reading Group |
19/07/2017 | Marius | 20+20 presentation on current research |
12/07/2017 | Stefan | 20+20 presentation on current research |
05/07/2017 | Jan | 20+20 presentation on current research |
14/06/2017 (15.00) | Katharina | Miikkulainen et al Evolving Deep Neural Networks |
17/05/2017 | Katharina | Preferential Bayesian Optimization Javier Gonzalez, Zhenwen Dai, Andreas Domianou, Neil D. Lawrence |
12/05/2017 | - | MSc thesis defense of Manuel Ruder (from the computer vision group) He did, e.g., this: https://arxiv.org/abs/1604.08610, https://www.youtube.com/watch?v=Khuj4ASldmU, and was already on TV with it. |
05/05/2017 | Marius | Michael Rudolph, Christina Hernández Wunsch |
07/04/2017 | Aaron | Mark McLeod, Michael A. Osborne, Stephen J. Roberts Practical Bayesian Optimization for Variable Cost Objectives |
31/03/2017 | Marius | N. Dang, L. Pérez Cáceres, T. Stützle, and P. De Causmaecker Configuring irace using surrogate configuration benchmarks http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2017-004.pdf |
24/03/2017 | Stefan | Olga Wichrowska et al. Learned Optimizers that Scale and Generalize |
17/03/2017 | Ilya | E. Real et al. Large-Scale Evolution of Image Classifiers |
09/03/2017 | Stefan | Zhao Chen and Darvin Yi The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI (https://arxiv.org/abs/1702.05663) Vlad Firoiu et al. Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning (https://arxiv.org/abs/1702.06230) |
10/02/2017 | Jan | Rijn, J.N. van Massively collaborative machine learning, Chapter 6 https://openaccess.leidenuniv.nl/handle/1887/44814 |
03/02/2017 | Ilya | On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang |
27/01/2017 | Matthias | Bayesian Hyperparameter Optimization for Ensemble Learning Julien-Charles Levesque, Christian Gagne and Robert Sabourin |
20/01/2017 | Jan | Hyperparameter Optimization Machines Martin Wistuba, Nicolas Schilling and Lars Schmidt-Thieme |
10/01/2017 | Marius | IMPORTANT: DATE CHANGED! The irace package: Iterated racing for automatic algorithm configuration http://www.sciencedirect.com/science/article/pii/S2214716015300270 |
2016 | ||
16/12/2016 | Markus Weimer | Markus Weimer leads the machine learning research group serving Azure ML, Azure Data Lake and Microsoft R Server and internal ML users at Microsoft. |
02/12/2016 | James | Learning to Learn for Global Optimization of Black Box Functions Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas |
25/11/2016 | Ilya | AN EMPIRICAL ANALYSIS OF DEEP NETWORK LOSS SURFACES Daniel Jiwoong Im, Michael Tao & Kristin Branson |
18/11/2016 | Aaron | Learning Curve Prediction with Bayesian Neural Networks Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter |
04/11/2016 | Marius | Yuriy Dzerin: Master Project Defense: Parallel Intensification Andre Biedenkapp: Master Thesis Introduction: Per-Instance Algorithm Configuration |
28/10/2016 | Jan | Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning Rui Leite and Pavel Brazdil |
21/10/2016 | Katharina | The Parallel Knowledge Gradient Method for Batch Bayesian Optimization Jian Wu, Peter I. Frazier |
14/10/2016 | Matthias | Scalable Hyperparameter Optimization with Products of Gaussian Process Experts http://link.springer.com/chapter/10.1007%2F978-3-319-46128-1_3 Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization Nicolas Schilling, Martin Wistuba and Lars Schmidt-Thieme http://link.springer.com/chapter/10.1007%2F978-3-319-46128-1_13 Martin Wistuba, Nicolas Schilling and Lars Schmidt-Thieme |
30/09/2016 | Stefan | Stefan Wager, Trevor Hastie and Bradley Efron Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife |
16/09/2016 | Marius | Matthias Poloczek, Jialei Wang, Peter I. Frazier Warm Starting Bayesian Optimization |
09/09/2016 | Ilya | COCO: Performance Assessment Nikolaus Hansen, Anne Auger, Dimo Brockhoff, Dejan Tušar and Tea Tušar |
02/09/2016 | Stefan | Statistical Comparisons of Classifiers over Multiple Data Sets Janez Demsar |
26/08/2016 | Aaron | Multi-fidelity Gaussian Process Bandit Optimisation Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos |
19/08/2016 | Katharina | Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke A Stratified Analysis of Bayesian Optimization Methods |
12/08/2016 | Matthias | Foster Provost, Tom Fawcett and Ron Kohavi The Case Against Accuracy Estimation for Comparing Induction Algorithms |
29/07/2016 | Marius | Random Survival Forests By Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone and Michael S. Lauer |
22/07/2016 11AM | Ilya | Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits |
15/07/2016 | Aaron | Bayesian optimization for automated model selection Gustavo Malkomes, Chip Schaff, Roman Garnett |
08/07/2016 | Stefan | Minimum Regret Search for Single- and Multi-Task Optimization Jan Hendrik Metzen In: Proceedings of The 33rd International Conference on Machine Learning, pp. 192–200, 2016 |
01/07/2016 | Katharina | Active Contextual Entropy Search Jan Hendrik Metzen, BayesOpt'15 |
16/06/2016 | Matthias | Locally-Biased Bayesian Optimization using Nonstationary Gaussian Processes Ruben Martinez-Cantin |
08.03.2016 10:00 | Aaron/ Katharina | GLASSES : Relieving The Myopia Of Bayesian Optimisation. J. González, M. Osborne, and N. Lawrence. In AISTATS’16. short: https://bayesopt.github.io/papers/2015/gonzalez.pdf long: http://arxiv.org/pdf/1510.06299v1.pdf Unbounded Bayesian Optimization via Regularization. B. Shahriari, A. Bouchard-Côté, and N. de Freitas, In AISTATS’16. http://arxiv.org/abs/1508.03666 |
08.03.2016 10:00 | Aaron/ Katharina | GLASSES : Relieving The Myopia Of Bayesian Optimisation. J. González, M. Osborne, and N. Lawrence. In AISTATS’16. short: https://bayesopt.github.io/papers/2015/gonzalez.pdf long: http://arxiv.org/pdf/1510.06299v1.pdf Unbounded Bayesian Optimization via Regularization. B. Shahriari, A. Bouchard-Côté, and N. de Freitas, In AISTATS’16. http://arxiv.org/abs/1508.03666 |
x | Marius | Deep Learning for Algorithm Selection by Andrea Loreggia, Yuri Malitsky, Horst Samulowitz, Vijay Saraswat - AAAI 2016 |
26.01.2016 10:30 | Matthias | Learning Hyperparameter Optimization Initializations Martin Witsuba, Nicolas Schilling, Lars Schmidt-Thieme http://www.ismll.uni-hildesheim.de/pub/pdfs/wistuba_et_al_DSAA_2015.pdf |
2015 | ||
01.12.2015 10:30 | Matthias | Fast Cross-Validation via Sequential Testing Tammo Krueger, Danny Panknin, Mikio Braun; 16(Jun):1103−1155, 2015. |
24.11.2015 10:30 | Aaron | Automating Model Search for Large Scale Machine Learning Sparks, Evan R and Talwalkar, Ameet and Haas, Daniel and Franklin, Michael J and Jordan, Michael I and Kraska, Tim. ACM Symposium on Cloud Computing 2015 (SoCC'15) https://amplab.cs.berkeley.edu/wp-content/uploads/2015/07/163-sparks.pdf |
20.11.2015 14:00 (cont. of 10.11.2015) | Stefan | Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design Niranjan Srinivas and Andreas Krause and Sham M. Kakade and Matthias Seger. ICML 2010 |
20.10.2015 10:30 | Stefan | Matthew Hoffman, Eric Brochu, Nando de Freitas Portfolio Allocation for Bayesian Optimization |
29/09/2015 11:00 | Matthias | Benchmarking Classification Algorithms on High-Performance Computing Clusters Bernd Bischl, Julia Schiffner and Claus Weihs http://rsrg.cms.caltech.edu/netecon/privacy2015/slides/hardt.pdf |
21/09/2015 10:00 | Stefan | Gambling in a rigged casino: The adversarial multi-armed bandit problem Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund and Robert E. Schapire |
17/08/2015 10:00 | Katharina | Statistical Regimes and Runtime Prediction Hurley, Barry and O’Sullivan, Barry. IJCAI’15 |
10/08/2015 10:00 | Mustafa | Collaborative Expert Portfolio Management http://research.microsoft.com/pubs/122781/matchboxqbf.pdf Matchbox: large scale online bayesian recommendations |
03/08/2015 10:00 | Mustafa | Neural fitted Q iteration--first experiences with a data efficient neural reinforcement learning method M. Riedmiller, ECML 2005 |
15/06/2015 10:00 | Stefan | Peter Auer, Nicolo Cesa-Bianchi and Paul Fischer Finite-time analysis of the multiarmed bandit problem, Machine Learning, 47 (2-3), 2002, Springer |
08/06/2015 | Ilya | Online Adaptation of Hyper-parameters in CMA-ES |
01/06/2015 | Stefan | Model-based Genetic Algorithms for Algorithm Configuration Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, Carlos Ansotegui, and Kevin Tierney https://drive.google.com/open?id=0B2py-37R9PftOV9URy1XYkhPRzQ&authuser=0 |
04/05/2015 10:00 | Stefan | An Entropy Search Portfolio for Bayesian Optimization Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas |
28/04/2015 10:00 | Marius | Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering. IJCAI 2013 |
20/04/2015 10:00 | Martin G. & David B | Martin’s Master Project on “Algorithm Configuration in the cloud” David’s Bachelor Project on “Instance-Specific Algorithm Schedules” |
01/06/2015 | Stefan | Model-based Genetic Algorithms for Algorithm Configuration Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, Carlos Ansotegui, and Kevin Tierney https://drive.google.com/open?id=0B2py-37R9PftOV9URy1XYkhPRzQ&authuser=0 |
04/05/2015 10:00 | Stefan | An Entropy Search Portfolio for Bayesian Optimization Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas |
28/04/2015 10:00 | Marius | Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering. IJCAI 2013 |
20/04/2015 10:00 | Martin G. & David B | Martin’s Master Project on “Algorithm Configuration in the cloud” David’s Bachelor Project on “Instance-Specific Algorithm Schedules” |
13/04/2015 10:00 | Aaron | Jose Miguel Hernandez-Lobato, Matthew W. Hoffman, Zoubin Ghahramani Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS 2014 |
30/03/2015 10:00 | Frank | Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. AISTATS 2014. |
23/03/2015 10:00 | Matthias | Caruana, Rich and Niculescu-Mizil, Alexandru and Crew, Geoff and Ksikes, Alex Proceedings of the twenty-first international conference on Machine learning. 2004 http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf Further reading: Rich Caruana, Art Munson, Alexandru Niculescu-Mizil Getting the Most Out of Ensemble Selection ICDM'06. (the paper is attached to the original paper in the PDF above) |
17/03/2015 | Mustafa | Mustafa Misir, Katja Verbeeck, Patrick De Causmaecker, Greet Vanden Berghe A new hyper-heuristic as a general problem solver: an implementation in HyFlex Journal of Scheduling, 16(3), 291-311, 2013 http://link.springer.com/article/10.1007/s10951-012-0295-8#page-1 |
10/03/2015 | Mustafa | Kent McClymont and Edward C. Keedwell(pdf)(bib) Markov chain hyper-heuristic (MCHH): an online selective hyper-heuristic for multi-objective continuous problems. GECCO 211 |
Suggested by | Suggested In | Paper |
Matthias | Jan’22 | Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning Tien-Dung Nguyen, David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys IJCNN’21 |
Nov’21 | Learning meta-features for AutoML | |
Dec’21 | Benchmarking Multimodal AutoML for Tabular Data with Text Fields Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander Smola | |
Nov | Pipeline Combinators for Gradual AutoML (OpenReview1, OpenReview2) Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay | |
Continual Learning in Practice | ||
How Much Automation Does a Data Scientist Want? | ||
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee, Niloufar Salehi, Aditya Parameswaran https://programs.sigchi.org/chi/2021/program/content/47544 | ||
AutoML for Predictive Maintenance: One Tool to RUL Them All Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (@ECML-PKDD) | ||
Adoption and Effects of Software Engineering Best Practices in Machine Learning | ||
Practices for Engineering Trustworthy Machine Learning Applications |
Suggested by | Suggested In | Paper |
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg, Su-In Lee (2017). NeurIPS 2017. | ||
Deep Neural Networks and Tabular Data: A Survey Borisov et al. arXiv |
Suggested by | Suggested In | Paper |
Output-Space Predictive Entropy Search for Flexible Global Optimization Matt Hoffman, Zoubin Ghahramani | ||
Frank | 05.2019 | Max-value entropy search |
05.2019 | Bayesian Optimization with Tree-structured Dependencies Rudolphe Jenatton, Cedric Archambeau, Javier González, Matthias Seeger In: ICML 2017 | |
Matthias | 08.2019 | Probabilistic Matrix Factorization for Automated Machine Learning Fusi, Sheth and Elibol http://papers.nips.cc/paper/7595-probabilistic-matrix-factorization-for-automated-machine-learning |
Suggested by | Suggested In | Paper |
Matthias | 05/2019 | Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl Tunability: Importance of Hyperparameters of Machine Learning Algorithms http://jmlr.org/papers/v20/18-444.html JMLR |
Marius | 2018 | Yasha Pushak, Holger H. Hoos Algorithm Configuration Landscapes: - More Benign Than Expected? http://www.cs.ubc.ca/labs/beta/Projects/ACLandscapes/ PPSN |
Matthias | 07/2019 | Meta-Learning for Black-box Optimization https://arxiv.org/pdf/1907.06901.pdf Accepted at ECML |
André | 07/2019 | Self-paced learning in latent variable models |
Frank | Learning a SAT Solver from Single-Bit Supervision | |
Matthias | 05/2019 | Random Forests, Decision Trees, and Categorical Predictors:The “Absent Levels” Problem |