Search Results for author: Weiran Shen

Found 10 papers, 4 papers with code

Optimal Vehicle Dispatching Schemes via Dynamic Pricing

no code implementations6 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.

Automated Mechanism Design via Neural Networks

no code implementations9 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.

Incremental training of multi-generative adversarial networks

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.

Learning to Clear the Market

no code implementations4 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.

Bayesian Nash Equilibrium in First-Price Auction with Discrete Value Distributions

1 code implementation22 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

Learning to Persuade

no code implementations29 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.

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

1 code implementation30 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.

P-MMF: Provider Max-min Fairness Re-ranking in Recommender System

1 code implementation12 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.

Fairness Recommendation Systems +1

LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

1 code implementation11 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.

Fairness Recommendation Systems

IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History

no code implementations24 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.

Imitation Learning Out-of-Distribution Generalization +1

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