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Chuan Yu

7 accepted papers

2026

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search

ICLR 2026oral

Auto-bidding serves as a critical tool for advertisers to improve their advertising performance. Recent progress has demonstrated that AI-Generated Bidding (AIGB), which learns a conditional generative planner from offline data, achieves superior performance compared to typical offline reinforcement…

Cited by 0SourceScholar
2026

RewardRRT: Path Planning for Multi-Degree-of-Freedom Robots in Narrow Environments

RA-L 2026

A novel path planning algorithm, RewardRRT, is proposed to address the challenge of Multi-degree-of-freedom robot path planning in narrow environments. In this approach, RewardRRT conceptualizes the sampling tree of Rapidly-exploring Random Trees (RRT) as an agent, assigning a reward value function

Cited by 0SourceScholar
2024

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

NeurIPS 2024spotlight

Decision-making in large-scale games is an essential research area in artificial intelligence (AI) with significant real-world impact. However, the limited access to realistic large-scale game environments has hindered research progress in this area. In this paper, we present AuctionNet, a benchmark…

Cited by 3SourcecodeScholar
2023

Truthful Auctions for Automated Bidding in Online Advertising

IJCAI 2023poster

Automated bidding, an emerging intelligent decision-making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based…

Cited by 11SourcePDFScholar
2023

Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising

AAAI 2023technical

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical utility maximizer model in auction theory not fit well. Some recent studie…

Cited by 11SourcePDFScholar
2022

Sustainable Online Reinforcement Learning for Auto-bidding

NeurIPS 2022accept

Recently, auto-bidding technique has become an essential tool to increase the revenue of advertisers. Facing the complex and ever-changing bidding environments in the real-world advertising system (RAS), state-of-the-art auto-bidding policies usually leverage reinforcement learning (RL) algorithms t…

2020

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

ICML 2020poster

In E-commerce, advertising is essential for merchants to reach their target users. The typical objective is to maximize the advertiser’s cumulative revenue over a period of time under a budget constraint. In real applications, an advertisement (ad) usually needs to be exposed to the same user multip…

Cited by 29SourcePDFScholar