no code implementations • 16 Apr 2024 • Lijun Liu, Jiali Yang, Jianfei Song, Xinglin Yang, Lele Niu, Zeqi Cai, Hui Shi, Tingjun Hou, Chang-Yu Hsieh, Weiran Shen, Yafeng Deng
Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations.
no code implementations • 24 Mar 2024 • Yi Xu, Weiran Shen, Xiao Zhang, Jun Xu
This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts.
1 code implementation • 11 Aug 2023 • Chen Xu, Xiaopeng Ye, Jun Xu, Xiao Zhang, Weiran Shen, Ji-Rong Wen
RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.
1 code implementation • 12 Mar 2023 • Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, Zhenghua Dong
In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem.
1 code implementation • 30 Aug 2022 • Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen, Chongxuan Li
Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits.
no code implementations • 29 Sep 2021 • Xiaodong Liu, Zhikang Fan, Xun Wang, Weiran Shen
Then we update the sender model to obtain an approximately optimal scheme using the receiver model.
1 code implementation • 22 Jun 2019 • Weiran Shen, Zihe Wang, Song Zuo
Some of the previous results in the case of continuous value distributions do not apply to the case of discrete value distributions.
Computer Science and Game Theory
no code implementations • 4 Jun 2019 • Weiran Shen, Sébastien Lahaie, Renato Paes Leme
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied.
no code implementations • ICLR 2019 • Qi Tan, Pingzhong Tang, Ke Xu, Weiran Shen, Song Zuo
Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set.
no code implementations • 9 May 2018 • Weiran Shen, Pingzhong Tang, Song Zuo
We then apply our framework to a number of multi-item revenue optimal design settings, for a few of which the theoretically optimal mechanisms are unknown.
no code implementations • 6 Jul 2017 • Mengjing Chen, Weiran Shen, Pingzhong Tang, Song Zuo
To this end, we use a so-called "ironing" technique to convert the problem into an equivalent convex optimization one via a clean Markov decision process (MDP) formulation, where the states are the driver distributions and the decision variables are the prices for each pair of locations.