AutoML 2022

20 papers

A Tree-Structured Multi-Task Model Recommender Lijun Zhang, Xiao Liu, Hui Guan

AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang

Automated Super-Network Generation for Scalable Neural Architecture Search Juan Pablo Munoz, Nikolay Lyalyushkin, Chaunte Willetta Lacewell, Anastasia Senina, Daniel Cummings, Anthony Sarah, Alexander Kozlov, Nilesh Jain

Automatic Termination for Hyperparameter Optimization Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau

Bayesian Generational Population-Based Training Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne

BERT-Sort: A Zero-Shot MLM Semantic Encoder on Ordinal Features for AutoML Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul Prasad

DIFER: Differentiable Automated Feature Engineering Guanghui Zhu, Zhuoer Xu, Chunfeng Yuan, Yihua Huang

Differentiable Architecture Search for Reinforcement Learning Yingjie Miao, Xingyou Song, John D Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso Kenan Šehić, Alexandre Gramfort, Joseph Salmon, Luigi Nardi

Meta-Adapters: Parameter Efficient Few-Shot Fine-Tuning Through Meta-Learning Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum

Non-Uniform Adversarially Robust Pruning Qi Zhao, Tim Königl, Christian Wressnegger

On the Optimality Gap of Warm-Started Hyperparameter Optimization Parikshit Ram

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin

Opening the Black Box: Automated Software Analysis for Algorithm Selection Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger Hoos

ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution Representation Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau

Tackling Neural Architecture Search with Quality Diversity Optimization Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl, Janek Thomas

What to Expect of Hardware Metric Predictors in NAS Kevin Alexander Laube, Maximus Mutschler, Andreas Zell

When, Where, and How to Add New Neurons to ANNs Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl