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AutoML 2022

20 papers

Differentiable Architecture Search for Reinforcement Learning

Yingjie Miao, Xingyou Song, Daiyi Peng, Summer Yue, Eugene Brevdo

Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning

Trapit Bansal, Salaheddin Alzubi, Tong Wang, Andrew McCallum, Jay Yoon Lee

When, where, and how to add new neurons to ANNs

Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson

DIFER: Differentiable Automated Feature Engineering

Guanghui Zhu, Zhuoer Xu, chunfeng yuan, Yihua Huang

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research

David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba

ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution Representation

Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan

Bayesian Generational Population-Based Training

Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen

What to expect of hardware metric predictors in NAS

Kevin Alexander Laube, Maximus Mutschler, Andreas Zell

On the Optimality Gap of Warm-Started Hyperparameter Optimization

Parikshit Ram

BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML

Mehdi Bahrami, Lei Liu, Mukul Prasad, Weipeng Chen, Wei-Peng Chen

A Tree-Structured Multi-Task Model Recommender

Lijun Zhang, Xiao Liu, Hui Guan

Tackling Neural Architecture Search With Quality Diversity Optimization

Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization

Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin

Automatic Termination for Hyperparameter Optimization

Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul

Opening the Black Box: Automated Software Analysis for Algorithm Selection

Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger Hoos

Automated Super-Network Generation for Scalable Neural Architecture Search

Juan Pablo Munoz, Nikolay Lyalyushkin, Chaunte Willetta Lacewell, Anastasia Senina, Daniel Cummings

AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks

Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization

Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso

Kenan Šehić, Alexandre Gramfort, Joseph Salmon, Luigi Nardi

Non-Uniform Adversarially Robust Pruning

Qi Zhao, Tim Königl, Christian Wressnegger