Accepted Papers
- Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alex M. Bronstein and Avi Mendelson
- Random Search and Reproducibility for Neural Architecture Search
Liam Li and Ameet Talwalkar
- A simple dynamic bandit algorithm for hyper-parameter tuning
Xuedong Shang, Emilie Kaufmann and Michal Valko
- Alpha MAML: Adaptive Model-Agnostic Meta-Learning
Harkirat Singh Behl, Atılım Güneş Baydin and Philip Torr
- MFITS: Bayesian active learning with support for continuous fidelity parameters and variable cost
Nicolas Knudde, Ivo Couckuyt and Tom Dhaene
- Toward Instance-aware Neural Architecture Search
An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei and Min Sun
- Learning the Learning Rate for Gradient Descent by Gradient Descent
Orchid Majumder, Michele Donini and Pratik Chaudhari
- Accelerating the Nelder - Mead Method with Predictive Parallel Evaluation
Yoshihiko Ozaki, Shuhei Watanabe and Masaki Onishi
- Explainability Constraints for Bayesian Optimization
Michael Li and Ryan Adams
- Graduated Optimisation of Black-Box Functions
Weijia Shao, Christian Geißler and Fikret Sivrikaya
- Bayesian Optimization over Sets
Jungtaek Kim, Michael McCourt, Saehoon Kim, Tackgeun You and Seungjin Choi
- From Switchable Normalization to Dynamic Normalization
Ping Luo, Zhanglin Peng, Wenqi Shao and Ruimao Zhang
- Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz and Debadeepta Dey
- Evolving Rewards to Automate Reinforcement Learning
Aleksandra Faust, Anthony Francis and Dar Mehta
- AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri and Corinna Cortes
- Improving Automated Variational Inference with Normalizing Flows
Stefan Webb, J. P. Chen, Martin Jankowiak and Noah Goodman
- Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
Matilde Gargiani, Aaron Klein, Stefan Falkner and Frank Hutter
- Meta-learning of textual representations
Jorge Madrid and Hugo Escalante
- ASCAI: Adaptive Sampling for acquiring Compact AI
Mojan Javaheripi, Mohammad Samragh, Tara Javidi and Farinaz Koushanfar
- Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim and Sungwoong Kim
- Meta-Learning Acquisition Functions for Bayesian Optimization
Michael Volpp, Lukas Fröhlich, Andreas Doerr, Stefan Falkner, Frank Hutter and Christian Daniel
- Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Iddo Drori, Yamuna Krishnamurthy, Raoni Paula Lourenco, Remi Rampin, Kyunghyun Cho, Claudio Silva and Juliana Freire
- Neural Architecture Search Over a Graph Search Space
Stanisław Jastrzębski, Quentin De Laroussilhe, Mingxing Tan, Xiao Ma, Neil Houlsby and Andrea Gesmundo
- Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents
Zalán Borsos, Andrey Khorlin and Andrea Gesmundo
- Evolutionary-Neural Hybrid Agents for Architecture Search
Krzysztof Maziarz, Andrey Khorlin, Quentin de Laroussilhe, Stanisław Jastrzębski, Mingxing Tan and Andrea Gesmundo
- Improving Neural Architecture Search Image Classifiers via Ensemble Learning
Vladimir Macko, Charles Weill, Hanna Mazzawi and Javier Gonzalvo
- Generative Teaching Networks: Machine learning algorithms that automatically generate training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley and Jeff Clune
- An Open Source AutoML Benchmark
Erin Ledell, Pieter Gijsbers, Joaquin Vanschoren, Janek Thomas, Bernd Bischl and Sebastien Poirier
- Automating Multi-Label Classification Extending ML-Plan
Marcel Wever, Felix Mohr, Alexander Hetzer and Eyke Hüllermeier
- A Boosting Tree Based AutoML System for Lifelong Machine Learning
Zheng Xiong, Wenpeng Zhang, Jiyan Jiang and Wenwu Zhu