Accepted Papers

Contributed Talks (presented in poster session 2)

  • Discovering Weight Initializers with Meta Learning

Dmitry Baranchuk and Artem Babenko.

PDF Poster

  • Multimodal AutoML on Structured Tables with Text Fields

Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li and Alex Smola.

PDF Poster

  • Automated Discovery of Adaptive Attacks on Adversarial Defenses

Chengyuan Yao, Pavol Bielik, Petar Tsankov and Martin Vechev.

PDF Poster

Poster Session 1

  • A resource-efficient method for repeated HPO and NAS problems

Giovanni Zappella, David Salinas and Cedric Archambeau.


  • Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed A Aly, Ganesh Venkatesh and Maximilian Balandat.

PDF Poster

  • GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery Implementation

Thomas Fletcher, Alan Bundy and Kwabena Nuamah.

PDF Poster

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

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

PDF Poster

  • Towards Model Selection using Learning Curve Cross-Validation

Felix Mohr and Jan N. van Rijn.

PDF Poster

  • Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio

Julien Niklas Siems, Aaron Klein, Cedric Archambeau and Maren Mahsereci.

PDF Poster

  • AutoML Adoption in ML Software

Koen Van der Blom, Alex Serban, Holger Hoos and Joost Visser.

PDF Poster

  • Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance

Parikshit Ram, Alexander G. Gray and Horst Samulowitz.

PDF Poster

  • Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization

Thomas Elsken, Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Simon Schrodi, André Biedenkapp, Difan Deng, Marius Lindauer, Frank Hutter and Guilherme Miotto.

PDF Poster

  • Towards Explaining Hyperparameter Optimization via Partial Dependence Plots

Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer and Bernd Bischl.

PDF Poster

  • Mutation is all you need

Lennart Schneider, Florian Pfisterer, Martin Binder and Bernd Bischl.

PDF Poster

  • Meta Learning the Step Size in Policy Gradient Methods

Luca Sabbioni, Francesco Corda and Marcello Restelli.

PDF Poster

Poster Session 2

  • Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation

Maxime Heuillet, Benoit Debaque and Audrey Durand.

PDF Poster

  • LRTuner: A Learning Rate Tuner for Deep Neural Networks

Nikhil Iyer, Thejas Venkatesh, Nipun Kwatra, Ramachandran Ramjee and Muthian Sivathanu.

PDF Poster

  • PonderNet: Learning to Ponder

Andrea Banino, Jan Balaguer and Charles Blundell.

PDF Poster (not yet available)

  • Replacing the Ex-Def Baseline in AutoML by Naive AutoML

Felix Mohr and Marcel Wever.

PDF Poster

  • Neural Fixed-Point Acceleration for Convex Optimization

Shobha Venkataraman and Brandon Amos.

PDF Poster

  • Ranking Architectures by Feature Extraction Capabilities

Debadeepta Dey, Shital Shah and Sebastien Bubeck.

PDF Poster

  • Incorporating domain knowledge into neural-guided search via in situ priors and constraints

Brenden K Petersen, Claudio Santiago and Mikel Landajuela.

PDF Poster

  • Tabular Data: Deep Learning is Not All You Need

Ravid Shwartz-Ziv and Amitai Armon.

PDF Poster

  • Automated Learning Rate Scheduler for Large-batch Training

Chiheon Kim, Saehoon Kim, Jongmin Kim, Donghoon Lee and Sungwoong Kim.

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  • Adaptation-Agnostic Meta-Training

Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu and Fu-lai Chung.

PDF Poster

  • On-the-fly learning of adaptive strategies with bandit algorithms

Rashid Bakirov, Damien Fay and Bogdan Gabrys.

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