165 citations · 266 across the 8 of their papers we have counts for
8 papers
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…
Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios
German Barquero, Johnny Núñez, Zhen Xu +4
Human behavior forecasting during human-human interactions is of utmost importance to provide robotic or virtual agents with social intelligence. This problem is especially challen…
Didn't see that coming: a survey on non-verbal social human behavior forecasting
German Barquero, Johnny Núñez, Sergio Escalera +4
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction a…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
Zhen Xu, Sergio Escalera, Isabelle Guyon +5
Obtaining standardized crowdsourced benchmark of computational methods is a major issue in data science communities. Dedicated frameworks enabling fair benchmarking in a unified en…
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge
Zhen Xu, Wei-Wei Tu, Isabelle Guyon
Analyzing better time series with limited human effort is of interest to academia and industry. Driven by business scenarios, we organized the first Automated Time Series Regressio…
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Ryan Turner, David Eriksson, Michael McCourt +4
This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importa…
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
Jingsong Wang, Tom Ko, Zhen Xu +4
The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. These tasks, which…