Publications (52)
From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction
Bencheng Yan, Yuejie Lei, Zhiyuan Zeng +7
Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark contrast to the {predictable scaling…
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'…
CUSIDE-T: Chunking, Simulating Future and Decoding for Transducer based Streaming ASR
Wenbo Zhao, Ziwei Li, Chuan Yu +1
Streaming automatic speech recognition (ASR) is very important for many real-world ASR applications. However, a notable challenge for streaming ASR systems lies in balancing operat…
Trends in the Diffusion of Misinformation on Social Media
Hunt Allcott, Matthew Gentzkow, Chuan Yu
We measure trends in the diffusion of misinformation on Facebook and Twitter between January 2015 and July 2018. We focus on stories from 570 sites that have been identified as pro…
Learning Optimal Deterministic Auctions with Correlated Valuation Distributions
Da Huo, Zhilin Zhang, Zhenzhe Zheng +3
In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve rev…
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…
RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment
Chenji Lu, Zhuo Chen, Hui Zhao +4
Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient co…
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…
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Chao Wen, Miao Xu, Zhilin Zhang +12
In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objecti…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
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…
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…
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
Siguang Huang, Yunli Wang, Lili Mou +4
Most machine learning classifiers only concern classification accuracy, while certain applications (such as medical diagnosis, meteorological forecasting, and computation advertisi…
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…
LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
Haoran Sun, Xinrui Song, Xinyu Zhang +7
The integration of advertising auction mechanisms into large language model (LLM)-based chatbots presents a significant opportunity for commercialization, yet poses unique challeng…
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng +7
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform…
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…
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…
MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding
Junxian Wu, Chenghan Fu, Zhanheng Nie +6
With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language…
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…
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…
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…
Gradient Coupling: The Hidden Barrier to Generalization in Agentic Reinforcement Learning
Jingyu Liu, Xiaopeng Wu, Jingquan Peng +4
Reinforcement learning (RL) is a dominant paradigm for training autonomous agents, yet these agents often exhibit poor generalization, failing to adapt to scenarios not seen during…
Truthful Auctions for Automated Bidding in Online Advertising
Yidan Xing, Zhilin Zhang, Zhenzhe Zheng +4
Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate…
Mn-doping induced ferromagnetism and enhanced superconductivity in Bi_4-x Mn_x O_4 S_3 (0.075 < = x < = 0.15)
Zhenjie Feng, Xunqing Yin, Yiming Cao +14
We demonstrate that Mn-doping in the layered sulfides Bi_4O_4S_3 leads to stable Bi_4-x Mn_x O_4 S_3 compounds that exhibit both long-range ferromagnetism and enhanced superconduct…
DARA: Few-shot Budget Allocation in Online Advertising via In-Context Decision Making with RL-Finetuned LLMs
Mingxuan Song, Yusen Huo, Bohan Zhou +5
Optimizing the advertiser's cumulative value of winning impressions under budget constraints poses a complex challenge in online advertising, under the paradigm of AI-Generated Bid…
Enhanced high-order harmonic generation in donor-doped band-gap materials
Chuan Yu, Kenneth K. Hansen, Lars Bojer Madsen
We find that a donor-doped band-gap material can enhance the overall high-order harmonic generation (HHG) efficiency by several orders of magnitude, compared with undoped and accep…
Sustainable Online Reinforcement Learning for Auto-bidding
Zhiyu Mou, Yusen Huo, Rongquan Bai +4
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 ad…
Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction
Zhaopei Huang, Qifeng Dai, Guozheng Wu +7
With the rise of smart personal devices, service-oriented human-agent interactions have become increasingly prevalent. This trend highlights the need for personalized dialogue assi…
XAV: A High-Performance Regular Expression Matching Engine for Packet Processing
Jincheng Zhong, Shuhui Chen, Chuan Yu
Regular expression matching is the core function of various network security applications such as network intrusion detection systems. With the network bandwidth increases, it is a…
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
Liyi Guo, Junqi Jin, Haoqi Zhang +10
Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial a…
Learning Adaptive Display Exposure for Real-Time Advertising
Weixun Wang, Junqi Jin, Jianye Hao +9
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. Howeve…
Diagnosing Task Insensitivity in Language Agents
Jingyu Liu, Xiaopeng Wu, Kehan Chen +2
Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key source of this failure as task in…
Hierarchically Constrained Adaptive Ad Exposure in Feeds
Dagui Chen, Qi Yan, Chunjie Chen +6
A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a sta…
Learning to Infer User Hidden States for Online Sequential Advertising
Zhaoqing Peng, Junqi Jin, Lan Luo +11
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both im…
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
Dagui Chen, Junqi Jin, Weinan Zhang +7
Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share si…
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…
Computation Resource Allocation Solution in Recommender Systems
Xun Yang, Yunli Wang, Cheng Chen +4
Recommender systems rely heavily on increasing computation resources to improve their business goal. By deploying computation-intensive models and algorithms, these systems are abl…
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…
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…
What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification
Guosheng Li, Fenghui Ren, Bin Liu +5
Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources -- annual reports, press…
Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising
Hongtao Lv, Zhilin Zhang, Zhenzhe Zheng +5
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 p…
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
Xiangyu Liu, Chuan Yu, Zhilin Zhang +10
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mecha…
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Xiaotian Hao, Zhaoqing Peng, Yi Ma +12
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 un…
MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Zhen Gong, Lvyin Niu, Yang Zhao +9
Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowad…
Subradiant Dimer Excitations of Emitter Chains Coupled to a 1D Waveguide
Yu-Xiang Zhang, Chuan Yu, Klaus Mølmer
This Letter shows that chains of optical or microwave emitters coupled to a 1D waveguide support subradiant states with close pairs of excited emitters, which have longer lifetimes…
Multi-channel Uplift Policy Learning
Changjian Liu, Tianyu Wang, Xiaoxuan Deng +7
The paper proposes ReAlloc, a causal teacher‑student framework for allocating fixed marketing budgets across multiple e‑commerce channels, using unbiased local gradients and long‑t…
Enhanced high-order harmonics through periodicity breaks: from backscattering to impurity states
Chuan Yu, Ulf Saalmann, Jan M. Rost
Backscattering of delocalized electrons has been recently established [Phys. Rev. A 105, L041101 (2022)] as a mechanism to enhance high-order harmonic generation (HHG) in periodic…
On Designing a Two-stage Auction for Online Advertising
Yiqing Wang, Xiangyu Liu, Zhenzhe Zheng +4
For the scalability of industrial online advertising systems, a two-stage auction architecture is widely used to enable efficient ad allocation on a large set of corpus within a li…
Crystal-momentum-resolved contributions to multiple plateaus of high-order harmonic generation from band-gap materials
Chuan Yu, Hossein Iravani, Lars Bojer Madsen
We study the crystal-momentum-resolved contributions to the high-order harmonic generation (HHG) in band-gap materials, and identify the relevant initial crystal momenta for the fi…
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,…
High harmonics from backscattering of delocalized electrons
Chuan Yu, Ulf Saalmann, Jan M. Rost
It is shown that electron backscattering can enhance high-harmonic generation in periodic systems with broken translational symmetry. Paradigmatically, we derive for a finite chain…