• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 21 matches

• 2022
• 
  Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul R. Prasad:
  BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML. AutoML 2022: 11/1-26
• 
  Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum:
  Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning. AutoML 2022: 19/1-18
• 
  Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen:
  ScaleNAS: Multi-Path One-Shot NAS for Scale-Aware High-Resolution Representation. AutoML 2022: 15/1-18
• 
  Duc N. M. Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang:
  AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks. AutoML 2022: 4/1-16
• 
  Kevin Alexander Laube, Maximus Mutschler, Andreas Zell:
  What to expect of hardware metric predictors in NAS. AutoML 2022: 13/1-15
• 
  Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis George Wilson:
  When, where, and how to add new neurons to ANNs. AutoML 2022: 18/1-12
• 
  Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau:
  Automatic Termination for Hyperparameter Optimization. AutoML 2022: 7/1-21
• 
  Yingjie Miao, Xingyou Song, John D. Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust:
  Differentiable Architecture Search for Reinforcement Learning. AutoML 2022: 20/1-17
• 
  Juan Pablo Muñoz, Nikolay Lyalyushkin, Chaunte Willetta Lacewell, Anastasia Senina, Daniel Cummings, Anthony Sarah, Alexander Kozlov, Nilesh Jain:
  Automated Super-Network Generation for Scalable Neural Architecture Search. AutoML 2022: 5/1-15
• 
  Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
  YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. AutoML 2022: 3/1-39
• 
  Damir Pulatov, Marie Anastacio, Lars Kotthoff, Holger H. Hoos:
  Opening the Black Box: Automated Software Analysis for Algorithm Selection. AutoML 2022: 6/1-18
• 
  Parikshit Ram:
  On the Optimality Gap of Warm-Started Hyperparameter Optimization. AutoML 2022: 12/1-14
• 
  David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau:
  Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML 2022: 16/1-23
• 
  Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
  Tackling Neural Architecture Search With Quality Diversity Optimization. AutoML 2022: 9/1-30
• 
  Kenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi:
  LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. AutoML 2022: 2/1-24
• 
  Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin:
  Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. AutoML 2022: 8/1-17
• 
  Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne:
  Bayesian Generational Population-Based Training. AutoML 2022: 14/1-27
• 
  Lijun Zhang, Xiao Liu, Hui Guan:
  A Tree-Structured Multi-Task Model Recommender. AutoML 2022: 10/1-12
• 
  Qi Zhao, Tim Köonigl, Christian Wressnegger:
  Non-Uniform Adversarially Robust Pruning. AutoML 2022: 1/1-16
• 
  Guanghui Zhu, Zhuoer Xu, Chunfeng Yuan, Yihua Huang:
  DIFER: Differentiable Automated Feature Engineering. AutoML 2022: 17/1-17
• 
  Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett:
  International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. Proceedings of Machine Learning Research 188, PMLR 2022 [contents]