1 code implementation • 17 Apr 2024 • Etienne David, Jean Bellot, Sylvain Le Corff
The main challenge is to model a rich variety of time series, leverage any available external signals and provide sharp predictions with statistical guarantees.
2 code implementations • 7 Feb 2022 • Etienne David, Jean Bellot, Sylvain Le Corff
Secondly, to leverage such a dataset, we propose a new hybrid forecasting model.
1 code implementation • ICLR 2022 • Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
no code implementations • 17 May 2021 • Etienne David, Mario Serouart, Daniel Smith, Simon Madec, Kaaviya Velumani, Shouyang Liu, Xu Wang, Francisco Pinto Espinosa, Shahameh Shafiee, Izzat S. A. Tahir, Hisashi Tsujimoto, Shuhei Nasuda, Bangyou Zheng, Norbert Kichgessner, Helge Aasen, Andreas Hund, Pouria Sadhegi-Tehran, Koichi Nagasawa, Goro Ishikawa, Sébastien Dandrifosse, Alexis Carlier, Benoit Mercatoris, Ken Kuroki, Haozhou Wang, Masanori Ishii, Minhajul A. Badhon, Curtis Pozniak, David Shaner LeBauer, Morten Lilimo, Jesse Poland, Scott Chapman, Benoit de Solan, Frédéric Baret, Ian Stavness, Wei Guo
We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version.
no code implementations • 13 May 2021 • Etienne David, Franklin Ogidi, Wei Guo, Frederic Baret, Ian Stavness
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.
6 code implementations • 14 Dec 2020 • Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.