3 citations · 7 across the 17 of their papers we have counts for
18 papers
Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
Changjian Liu, Tianyu Wang, Yuwei Xu +6
Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-stationarity poses a fundamental ch…
Multi-channel Uplift Policy Learning
Changjian Liu, Tianyu Wang, Xiaoxuan Deng +7
E-commerce platforms must allocate fixed marketing budgets across multiple channels to maximize business utility. However, standard predict-then-optimize (PTO) paradigms fail in th…
AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization
Yuejia Dou, Hesong Wang, Xinyu Zhang +6
Auto-bidding plays an essential role in online advertising, automatically adjusting bids for advertisers to optimize their commercial goals. The emerging AI-Generated Bidding (AIGB…
DecisionLLM: Large Language Models for Long Sequence Decision Exploration
Xiaowei Lv, Zhilin Zhang, Yijun Li +10
Long-sequence decision-making, which is usually addressed through reinforcement learning (RL), is a critical component for optimizing strategic operations in dynamic environments,…
Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens
Jiahao Ji, Tianyu Wang, Yeshu Li +5
Auto-bidding is crucial in facilitating online advertising by automatically providing bids for advertisers. While previous work has made great efforts to model bidding environments…
HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments
Qi Li, Wendong Huang, Qichen Ye +9
Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding. Auction mechanisms vary across channels in ranking rules (p…
VAO: Validation-Aligned Optimization for Cross-Task Generative Auto-Bidding
Yiqin Lv, Zhiyu Mou, Miao Xu +9
Generative auto-bidding has demonstrated strong performance in online advertising, yet it often suffers from data scarcity in small-scale settings with limited advertiser participa…
Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Zhiyu Mou, Yiqin Lv, Miao Xu +9
Auto-bidding is a critical tool for advertisers to improve advertising performance. Recent progress has demonstrated that AI-Generated Bidding (AIGB), which learns a conditional ge…
Beyond Advertising: Mechanism Design for Platform-Wide Marketing Service "QuanZhanTui"
Ningyuan Li, Zhilin Zhang, Tianyan Long +8
On e-commerce platforms, sellers typically bid for impressions from ad traffic to promote their products. However, for most sellers, the majority of their sales come from organic t…
Permutation Equivariant Model-based Offline Reinforcement Learning for Auto-bidding
Zhiyu Mou, Miao Xu, Wei Chen +3
Reinforcement learning (RL) for auto-bidding has shifted from using simplistic offline simulators (Simulation-based RL Bidding, SRLB) to offline RL on fixed real datasets (Offline…
Large-Scale Auto-bidding with Nash Equilibrium Constraints
Zhiyu Mou, Miao Xu, Rongquan Bai +4
Auto-bidding has become a cornerstone of modern online advertising platforms, enabling many advertisers to automate bidding at scale and optimize campaign performance. However, pre…
An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising
Zhijian Duan, Yusen Huo, Tianyu Wang +6
In online advertising, advertisers commonly utilize auto-bidding services to bid for impression opportunities. A typical objective of the auto-bidder is to optimize the advertiser'…
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
Kefan Su, Yusen Huo, Zhilin Zhang +5
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 larg…
Contextual Generative Auction with Permutation-level Externalities for Online Advertising
Ruitao Zhu, Yangsu Liu, Dagui Chen +7
Online advertising has become a core revenue driver for the internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Tradi…
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling
Jiayan Guo, Yusen Huo, Zhilin Zhang +5
Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bid…
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
Haoming Li, Yusen Huo, Shuai Dou +5
In online advertising, advertisers participate in ad auctions to acquire ad opportunities, often by utilizing auto-bidding tools provided by demand-side platforms (DSPs). The curre…
Automated Deterministic Auction Design with Objective Decomposition
Zhijian Duan, Haoran Sun, Yichong Xia +6
Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design. While t…
Learning against Non-credible Auctions
Qian Wang, Xuanzhi Xia, Zongjun Yang +7
The standard framework of online bidding algorithm design assumes that the seller commits himself to faithfully implementing the rules of the adopted auction. However, the seller m…