98 citations · 264 across the 24 of their papers we have counts for
5 papers · 1 filter
Towards AutoML in the presence of Drift: first results
Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales +5
Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn. However, so far the…
Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
Lijun Zhang, Guanghui Wang, Wei-Wei Tu +1
To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the…
Efficient Neural Architecture Search via Proximal Iterations
Quanming Yao, Ju Xu, Wei-Wei Tu +1
Neural architecture search (NAS) recently attracts much research attention because of its ability to identify better architectures than handcrafted ones. However, many NAS methods,…
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Yuanfei Luo, Mengshuo Wang, Hao Zhou +5
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross,…
AutoML @ NeurIPS 2018 challenge: Design and Results
Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon +5
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participa…