AutoML 2022 Track Main Submissions

GSparsity: Unifying Network Pruning and Neural Architecture Search by Group Sparsity
Avraam Chatzimichailidis, Arber Zela, Janis Keuper, Yang Yang
- 25 Feb 2022 (modified: 05 May 2023)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
Graph Embedding for Neural Architecture Search with Input-Output Information
Gabriela Suchopárová, Roman Neruda
- 25 Feb 2022 (modified: 05 May 2023)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
Automatic Termination for Hyperparameter Optimization
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
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
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy
Badr AlKhamissi, Akshay Srinivasan, Zeb Kurth-Nelson, Samuel Ritter
- 25 Feb 2022 (modified: 03 Nov 2024)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML
Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul Prasad
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
Self-Optimizing Random Forests
Felix Mohr
- 25 Feb 2022 (modified: 05 May 2023)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning
Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks
Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities
Daniel Cummings, Anthony Sarah, Sharath Nittur Sridhar, Maciej Szankin, Juan Pablo Munoz, Sairam Sundaresan
- 25 Feb 2022 (modified: 03 Nov 2024)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
Bayesian Generational Population-Based Training
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne
- Published: 16 May 2022, Last Modified: 03 Nov 2024
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
Tackling Neural Architecture Search With Quality Diversity Optimization
Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl, Janek Thomas
- Published: 16 May 2022, Last Modified: 03 Nov 2024
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization
Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
Non-Uniform Adversarially Robust Pruning
Qi Zhao, Tim Königl, Christian Wressnegger
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
When, where, and how to add new neurons to ANNs
Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson
- Published: 16 May 2022, Last Modified: 03 Nov 2024
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
Searching Efficient Dynamic Graph CNN for Point Cloud Processing
Panyue Chen, Rui Wang, Ping Zhao, Guanming Liu, Zhihua Wei
- 25 Feb 2022 (modified: 05 May 2023)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
Regularized Meta-Learning for Neural Architecture Search
Rob van Gastel, Joaquin Vanschoren
- 25 Feb 2022 (modified: 05 May 2023)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
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
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
Opening the Black Box: Automated Software Analysis for Algorithm Selection
Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger Hoos
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
PASHA: Efficient HPO with Progressive Resource Allocation
Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cedric Archambeau, Giovanni Zappella
- 25 Feb 2022 (modified: 03 Nov 2024)
- AutoML 2022 (Late-Breaking Workshop)
- Readers: Everyone
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon, Luigi Nardi
- Published: 16 May 2022, Last Modified: 03 Nov 2024
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
DIFER: Differentiable Automated Feature Engineering
Guanghui Zhu, Zhuoer Xu, Chunfeng Yuan, Yihua Huang
- Published: 16 May 2022, Last Modified: 03 Nov 2024
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin
- Published: 16 May 2022, Last Modified: 03 Nov 2024
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone
On the Optimality Gap of Warm-Started Hyperparameter Optimization
Parikshit Ram
- Published: 16 May 2022, Last Modified: 05 May 2023
- AutoML-Conf 2022 (Main Track)
- Readers: Everyone