no code implementations • 22 Feb 2024 • Yurong Chen, Zhaohua Chen, Xiaotie Deng, Zhiyi Huang
This paper considers the hidden-action model of the principal-agent problem, in which a principal incentivizes an agent to work on a project using a contract.
no code implementations • 19 Feb 2024 • Zhijian Duan, Haoran Sun, Yichong Xia, Siqiang Wang, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng, Xiaotie Deng
Subsequently, we propose a novel optimization method that combines both zeroth-order and first-order techniques to optimize the VVCA parameters.
no code implementations • 18 Dec 2023 • Hanyu Li, Wenhan Huang, Zhijian Duan, David Henry Mguni, Kun Shao, Jun Wang, Xiaotie Deng
This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives.
no code implementations • 7 Dec 2023 • Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng
In this paper, we investigate a problem of actively learning threshold in latent space, where the unknown reward $g(\gamma, v)$ depends on the proposed threshold $\gamma$ and latent value $v$ and it can be $only$ achieved if the threshold is lower than or equal to the unknown latent value.
no code implementations • 12 Oct 2023 • Xiaotie Deng, Dongchen Li, Hanyu Li
For the first time, this work provides an automatic method for approximation analysis on a well-studied problem in theoretical computer science: computing approximate Nash equilibria in two-player games.
no code implementations • 13 Jun 2023 • Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng
To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.
2 code implementations • NeurIPS 2023 • Zhijian Duan, Haoran Sun, Yurong Chen, Xiaotie Deng
AMenuNet is always DSIC and individually rational (IR) due to the properties of AMAs, and it enhances scalability by generating candidate allocations through a neural network.
no code implementations • 23 Feb 2023 • Yurong Chen, Xiaotie Deng, Jiarui Gan, Yuhao Li
We consider the scenario where the follower is not given any information about the leader's payoffs to begin with but has to learn to manipulate by interacting with the leader.
no code implementations • 27 Jan 2023 • Zhijian Duan, Yunxuan Ma, Xiaotie Deng
Recently, remarkable progress has been made by approximating Nash equilibrium (NE), correlated equilibrium (CE), and coarse correlated equilibrium (CCE) through function approximation that trains a neural network to predict equilibria from game representations.
no code implementations • 25 Nov 2022 • Rui Ai, Zhaohua Chen, Xiaotie Deng, Yuqi Pan, Chang Wang, Mingwei Yang
To the best of our knowledge, this is the first $\widetilde O(1)$ regret result in the CBwK problem regardless of information feedback models.
no code implementations • 3 Nov 2022 • Hongyin Chen, Xiaotie Deng, Ying Wang, Yue Wu, Dengji Zhao
A diffusion auction is a market to sell commodities over a social network, where the challenge is to incentivize existing buyers to invite their neighbors in the network to join the market.
no code implementations • journal 2022 • Jiarui Zhang, Yukun Cheng, Xiaotie Deng
First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified.
no code implementations • 11 Jul 2022 • Zhaohua Chen, Chang Wang, Qian Wang, Yuqi Pan, Zhuming Shi, Zheng Cai, Yukun Ren, Zhihua Zhu, Xiaotie Deng
Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in.
no code implementations • 27 Jun 2022 • Yurong Chen, Xiaotie Deng, Yuhao Li
For all the settings considered in this paper, we characterize all the possible game outcomes that can be induced successfully.
no code implementations • 29 May 2022 • Rui Ai, Chang Wang, Chenchen Li, Jinshan Zhang, Wenhan Huang, Xiaotie Deng
Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions.
no code implementations • 3 May 2022 • Yurong Chen, Xiaotie Deng, Chenchen Li, David Mguni, Jun Wang, Xiang Yan, Yaodong Yang
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms.
no code implementations • 4 Apr 2022 • Xiaotie Deng, Hanyu Li, Ningyuan Li
An extension of this proof presents a geometric characterization of the set of stationary points of $f$.
1 code implementation • 29 Jan 2022 • Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue.
1 code implementation • 8 Oct 2021 • Xiaotie Deng, Xinyan Hu, Tao Lin, Weiqiang Zheng
Specifically, the results depend on the number of bidders with the highest value: - If the number is at least three, the bidding dynamics almost surely converges to a Nash equilibrium of the auction, both in time-average and in last-iterate.
no code implementations • 4 Sep 2021 • Xiaotie Deng, Ningyuan Li, David Mguni, Jun Wang, Yaodong Yang
Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 17 Aug 2021 • Zhijian Duan, Wenhan Huang, Dinghuai Zhang, Yali Du, Jun Wang, Yaodong Yang, Xiaotie Deng
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution.
no code implementations • NeurIPS 2020 • Xiaotie Deng, Ron Lavi, Tao Lin, Qi Qi, Wenwei Wang, Xiang Yan
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserve prices for revenue-maximizing auctioneers in both repeated auctions and uniform-price auctions.
no code implementations • 19 Jan 2020 • Mengqian Zhang, Jichen Li, Zhaohua Chen, Hongyin Chen, Xiaotie Deng
Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes.
Distributed, Parallel, and Cluster Computing Cryptography and Security
1 code implementation • 26 Nov 2018 • Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.
no code implementations • 19 Nov 2018 • Chenchen Li, Jialin Wang, Hongwei Wang, Miao Zhao, Wenjie Li, Xiaotie Deng
To enhance the emotion discriminativeness of words in textual feature extraction, we propose Emotional Word Embedding (EWE) to learn text representations by jointly considering their semantics and emotions.
no code implementations • 23 Aug 2018 • Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong
Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.