Search Results for author: Xiaotie Deng

Found 28 papers, 4 papers with code

Hummer: Towards Limited Competitive Preference Dataset

no code implementations19 May 2024 Li Jiang, Yusen Wu, Junwu Xiong, Jingqing Ruan, Yichuan Ding, Qingpei Guo, Zujie Wen, Jun Zhou, Xiaotie Deng

Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedback.

Are Bounded Contracts Learnable and Approximately Optimal?

no code implementations22 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.

Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method

no code implementations19 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.

A survey on algorithms for Nash equilibria in finite normal-form games

no code implementations18 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.

Learning Thresholds with Latent Values and Censored Feedback

no code implementations7 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.

The Search-and-Mix Paradigm in Approximate Nash Equilibrium Algorithms

no code implementations12 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.


Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

no code implementations13 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.

A Scalable Neural Network for DSIC Affine Maximizer Auction Design

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.

Learning to Manipulate a Commitment Optimizer

no code implementations23 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.

Are Equivariant Equilibrium Approximators Beneficial?

no code implementations27 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.

On the Re-Solving Heuristic for (Binary) Contextual Bandits with Knapsacks

no code implementations25 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.

Management Multi-Armed Bandits

Sybil-Proof Diffusion Auction in Social Networks

no code implementations3 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.

AReputation-Based Mechanism for Transaction Processing in Blockchain Systems

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.

Dynamic Budget Throttling in Repeated Second-Price Auctions

no code implementations11 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.

Optimal Private Payoff Manipulation against Commitment in Extensive-form Games

no code implementations27 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-regret Learning in Repeated First-Price Auctions with Budget Constraints

no code implementations29 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.

Survival Analysis

On the Convergence of Fictitious Play: A Decomposition Approach

no code implementations3 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.

On Convergence Lemma and Convergence Stability for Piecewise Analytic Functions

no code implementations4 Apr 2022 Xiaotie Deng, Hanyu Li, Ningyuan Li

An extension of this proof presents a geometric characterization of the set of stationary points of $f$.


A Context-Integrated Transformer-Based Neural Network for Auction Design

1 code implementation29 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.

Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions

1 code implementation8 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.

On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games

no code implementations4 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

Is Nash Equilibrium Approximator Learnable?

no code implementations17 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.

BIG-bench Machine Learning Meta-Learning +1

A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling

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.

CycLedger: A Scalable and Secure Parallel Protocol for Distributed Ledger via Sharding

no code implementations19 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

Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks

no code implementations19 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.

Emotion Recognition MULTI-VIEW LEARNING

Latent Dirichlet Allocation for Internet Price War

no code implementations23 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.

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