no code implementations • ICML 2020 • Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel
We experiment with three structured bandit problems: cascading bandits, online learning to rank in the position-based model, and rank-1 bandits.
2 code implementations • 21 Jun 2021 • Jianpeng Chen, Yujing Wang, Ming Zeng, Zongyi Xiang, Bitan Hou, Yunhai Tong, Ole J. Mengshoel, Yazhou Ren
Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph.
no code implementations • 9 Jul 2020 • Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel
We propose a novel framework for structured bandits, which we call an influence diagram bandit.
no code implementations • 21 Nov 2019 • Bitan Hou, Yujing Wang, Ming Zeng, Shan Jiang, Ole J. Mengshoel, Yunhai Tong, Jing Bai
For these applications, graph embedding is crucial as it provides vector representations of the graph.
no code implementations • 6 Nov 2018 • Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan Gardner, Daniel Genin, Joshua Silbermann, Michael Owen, Mykel J. Kochenderfer
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars.
no code implementations • 7 Oct 2018 • Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian Lane, Xiaobing Liu
To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention.
no code implementations • 22 Jan 2018 • Ming Zeng, Tong Yu, Xiao Wang, Le T. Nguyen, Ole J. Mengshoel, Ian Lane
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity.
no code implementations • 22 Nov 2017 • Bing Liu, Tong Yu, Ian Lane, Ole J. Mengshoel
Moreover, we report encouraging response selection performance of the proposed neural bandit model using the Recall@k metric for a small set of online training samples.
no code implementations • 21 Sep 2017 • Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel
We study the problem of learning a latent variable model from a stream of data.
no code implementations • 30 Aug 2017 • Ritchie Lee, Mykel J. Kochenderfer, Ole J. Mengshoel, Joshua Silbermann
In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data.