1 code implementation • 8 Feb 2024 • Wenjie Xu, Wenbin Wang, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions.
no code implementations • 2 Oct 2023 • Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
Additionally, the algorithm guarantees an $\mathcal{O}(N\sqrt{T})$ bound on the cumulative violation for the known affine constraints, where $N$ is the number of agents.
no code implementations • 1 Oct 2023 • Wenjie Xu, Bratislav Svetozarevic, Loris Di Natale, Philipp Heer, Colin N Jones
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold.
no code implementations • 8 Jun 2023 • Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
We consider the problem of optimizing a grey-box objective function, i. e., nested function composed of both black-box and white-box functions.
1 code implementation • 13 May 2023 • Wenjie Xu, Ben Liu, Miao Peng, Xu Jia, Min Peng
We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models.
1 code implementation • 12 Apr 2023 • Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances.
1 code implementation • 28 Jan 2023 • Wenjie Xu, Colin N Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
no code implementations • 21 Nov 2022 • Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints.
1 code implementation • 10 Oct 2022 • Miao Peng, Ben Liu, Qianqian Xie, Wenjie Xu, Hua Wang, Min Peng
Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information.
no code implementations • 4 Oct 2022 • Wenjie Xu, Titing Cui, Minghua Chen
The FPTAS can achieve a fuel consumption within a ratio of $(1+\epsilon)$ to the optimal (for any $\epsilon>0$) with a time complexity polynomial in the size of the transportation network and $1/\epsilon$.
no code implementations • 20 Sep 2022 • Wenjie Xu, Yuning Jiang, Emilio T. Maddalena, Colin N. Jones
In this paper, we study the worst-case complexity of the efficient global optimization problem and, in contrast to existing kernel-specific results, we derive a unified lower bound for the complexity of efficient global optimization in terms of the metric entropy of a ball in its corresponding reproducing kernel Hilbert space~(RKHS).
1 code implementation • 21 Feb 2022 • Xingye Chen, Yiqi Wu, Wenjie Xu, Jin Li, Huaiyi Dong, Yilin Chen
This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud.
Ranked #41 on 3D Point Cloud Classification on ModelNet40
no code implementations • 14 Oct 2021 • Wenjie Xu, Colin N Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics.
3 code implementations • 15 Sep 2020 • Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, Jie Liu, Jie Tang, Gangshan Wu, Yu Zhu, Xiangyu He, Wenjie Xu, Chenghua Li, Cong Leng, Jian Cheng, Guangyang Wu, Wenyi Wang, Xiaohong Liu, Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Xiaochuan Li, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Abdul Muqeet, Jiwon Hwang, Subin Yang, JungHeum Kang, Sung-Ho Bae, Yongwoo Kim, Geun-Woo Jeon, Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee, Steven Marty, Eric Marty, Dongliang Xiong, Siang Chen, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Haicheng Wang, Vineeth Bhaskara, Alex Levinshtein, Stavros Tsogkas, Allan Jepson, Xiangzhen Kong, Tongtong Zhao, Shanshan Zhao, Hrishikesh P. S, Densen Puthussery, Jiji C. V, Nan Nan, Shuai Liu, Jie Cai, Zibo Meng, Jiaming Ding, Chiu Man Ho, Xuehui Wang, Qiong Yan, Yuzhi Zhao, Long Chen, Jiangtao Zhang, Xiaotong Luo, Liang Chen, Yanyun Qu, Long Sun, Wenhao Wang, Zhenbing Liu, Rushi Lan, Rao Muhammad Umer, Christian Micheloni
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
no code implementations • 25 Sep 2019 • Wenjie Xu, Xiuqiong Chen, Stephen S.-T. Yau
Another type of popular neural network, deep (feed-forward) neural network has also been successfully applied in different engineering disciplines, whose approximation capability has been well characterized by universal approxi- mation theorem (Hornik et al. (1989), Park & Sandberg (1991), Lu et al. (2017)).