Invited talks
Accepted papers 37 Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph Gonzalez and Ion Stoica. Demo Tune: A Research Platform for Distributed Model Selection and Training Paper 50 Jorge G Madrid, Hugo J Escalante, Eduardo Morales, Wei Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon and Michele Sebag. Towards AutoML in the presence of Drift: first results (Contributed Talk 3) Paper 6 Casey Davis and Christophe Giraud-Carrier. Annotative Experts for Hyperparameter Selection Paper 7 Kunkun Pang, Mingzhi Dong, Yang Wu and Timothy Hospedales. Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning (Contributed Talk 2) Paper 12 Mark McLeod, Michael A. Osborne and Stephen J Roberts. Adaptive Quadrature for Fast Sequential Hyperparameter Marginalization Paper 19 Esteban Real, Alok Aggarwal, Yanping Huang and Quoc V. Le. Evolutionary Algorithms and Reinforcement Learning: A Comparative Case Study for Architecture Search (Contributed Talk 4) Paper 35 Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Jorge Piazentin Ono, Kyunghyun Cho, Claudio Silva and Juliana Freire. AlphaD3M: Machine Learning Pipeline Synthesis (Contributed Talk 1) Paper 43 Scott Langevin, David Jonker, Chris Bethune, Glen Coppersmith, Casey Hilland, Jonathon Morgan, Paul Azunre and Justin Gawrilow. Demo Distil: A Mixed-Initiative Model Discovery System for Subject Matter Experts Paper 8 Erin Grant, Ghassen Jerfel, Katherine Heller and Thomas L. Griffiths. Augmenting Gradient-Based Meta-Learning with Latent Variables to Capture Task Heterogeneity Paper 17 Yolanda Gil, Ke-Thia Yao, Varun Ratnakar, Daniel Garijo, Greg Ver Steeg, Pedro Szekely, Robert Brekelmans, Mayank Kejriwal, Fanghao Luo and I-Hui Huang. P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning Paper 4 Janek Thomas, Stefan Coors and Bernd Bischl. Demo Efficient Neural Architecture Search with Network Morphism Paper 13 Matthias Feurer, Benjamin Letham and Eytan Bakshy. Scalable Meta-Learning for Bayesian Optimization using Ranking-Weighted Gaussian Process Ensembles Paper 16 Prajit Ramachandran and Quoc Le. Dynamic Network Architectures Paper 22 Christos Tsirigotis, Xavier Bouthillier, François Corneau-Tremblay, Dendi Suhubdy, Frédéric Bastien and Pascal Lamblin. Demo Orion: Experiment Version Control for Efficient Hyperparameter Optimization Paper 26 Eleni Nisioti, Kyriakos Chatzidimitriou and Andreas Symeonidis. Predicting hyperparameters from meta-features in binary classification problems Paper 29 Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin and Richard Turner. Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning Paper 40 Gellert Weisz, Andras Gyorgy and Csaba Szepesvari. CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration Paper 46 Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter. Practical Automated Machine Learning for the AutoML Challenge 2018 Paper 47 Paul Azunre, Craig Corcoran, David Sullivan, Garrett Honke, Rebecca Ruppel, Sandeep Verma and Jonathon Morgan. Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings Paper 49 Arber Zela, Aaron Klein, Stefan Falkner and Frank Hutter. Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search Paper 51 Matthias Feurer and Frank Hutter. Towards Further Automation in AutoML Paper 52 Brandon Schoenfeld, Christophe Giraud-Carrier, Mason Poggeman, Jarom Christensen and Kevin Seppi. Preprocessor Selection for Machine Learning Pipelines Paper 55 Purushotham Kamath, Abhishek Singh and Debo Dutta. Demo AMLA: an AutoML frAmework for Neural Network Design Paper 56 Neil Dhir, Davide Zilli, Tomasz Rudny and Alessandra Tosi. Automatic Type Inference with a Nested Latent Variable Model Paper 64 Carlos Vieira, Adelson Araújo Júnior, Deângela C. G. Neves and Leonardo C. T. Bezerra. iSklearn: Assessing irace for automated machine learning 20 José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Michael A. Gelbart, Brandon Reagen, Rob Adolf, Paul N. Whatmough, David Brooks and Gu-Yeon Wei. A Comparison of Several Models on Hardware Accelerator Data for Deep Neural Networks Design using Bayesian Optimization Paper 21 Jungtaek Kim and Seungjin Choi. Automated Machine Learning for Soft Voting in an Ensemble of Tree-based Classifiers Paper 30 Rauf Izmailov, Peter Lin and Chumki Basu. Automatic Feature Selection in Learning Using Privileged Information Paper 31 Man-Ling Sung, Jan Silovsky, Man-Hung Siu, Herbert Gish and Chinnu Pittapally. Neural Network Conversion of Machine Learning Pipelines Paper 33 Maximilian Alber, Irwan Bello, Barret Zoph, Pieter-Jan Kindermans, Prajit Ramachandran and Quoc Le. Bayesian Optimization Exploiting Monotonicity and Its Application in Machine Learning Hyperparameter Tuning Paper 63 Patricio Cerda, Gaël Varoquaux and Balázs Kégl. Similarity encoding for learning with dirty categorical data Paper 23 Eduardo Garrido-Merchán and Daniel Hernández-Lobato. Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes Paper 38 Marcel Wever, Felix Mohr and Eyke Hüllermeier. ML-Plan for Unlimited-Length Machine Learning Pipelines Paper 15 Herilalaina Rakotoarison and Michèle Sebag. Paper AutoML with Monte Carlo Tree Search 39 Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo and Paolo Frasconi. Demo Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning Paper 48 Maria João Ferreira and Pavel Brazdil. Workflow Recommendation for Text Classification with Active Testing Method Paper 58 Udayan Khurana, Horst Samulowitz and Deepak Turaga. Paper Ensembles with Automated Feature Engineering |
