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Augmenting the input of algorithms with predictions is an algorithm design paradigm that suggests leveraging a (possibly erroneous) prediction to improve worst-case performance guarantees when the prediction is perfect (consistency), while…

Computer Science and Game Theory · Computer Science 2025-11-20 Georgios Amanatidis , Evangelos Markakis , Christodoulos Santorinaios , Guido Schäfer , Panagiotis Tsamopoulos , Artem Tsikiridis

Bid optimization in online advertising relies on black-box machine-learning models that learn bidding decisions from historical data. However, these approaches fail to replicate human experts' adaptive, experience-driven, and globally…

Artificial Intelligence · Computer Science 2026-03-06 Huixiang Luo , Longyu Gao , Yaqi Liu , Qianqian Chen , Pingchun Huang , Tianning Li

We consider some classical optimization problems in path planning and network transport, and we introduce new auction-based algorithms for their optimal and suboptimal solution. The algorithms are based on mathematical ideas that are…

Optimization and Control · Mathematics 2022-07-21 Dimitri Bertsekas

Online advertisements are a primary revenue source for e-commerce platforms. Traditional advertising models are store-centric, selecting winning stores through auction mechanisms. Recently, a new approach known as joint advertising has…

Computer Science and Game Theory · Computer Science 2025-07-11 Zhen Zhang , Weian Li , Yuhan Wang , Qi Qi , Kun Huang

Combinatorial auctions (CA) are a well-studied area in algorithmic mechanism design. However, contrary to the standard model, empirical studies suggest that a bidder's valuation often does not depend solely on the goods assigned to him. For…

Computer Science and Game Theory · Computer Science 2015-10-01 Yun Kuen Cheung , Monika Henzinger , Martin Hoefer , Martin Starnberger

Auto-bidding has become one of the main options for bidding in online advertisements, in which advertisers only need to specify high-level objectives and leave the complex task of bidding to auto-bidders. In this paper, we propose a family…

Computer Science and Game Theory · Computer Science 2021-03-25 Yuan Deng , Jieming Mao , Vahab Mirrokni , Song Zuo

We study the problem of learning to bid when the bidder's value is dynamic, i.e., when the current value depends on past outcomes. Specifically, we consider a bidder participating in repeated second-price auctions whose value depends on the…

Machine Learning · Computer Science 2026-05-28 Benjamin Heymann , Otmane Sakhi

In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue…

Information Retrieval · Computer Science 2022-06-09 Shinya Suzumura , Hitoshi Abe

Internet live streaming is widely used in online entertainment and e-commerce, where live advertising is an important marketing tool for anchors. An advertising campaign hopes to maximize the effect (such as conversions) under constraints…

Machine Learning · Statistics 2025-08-11 Bo Yang , Ruixuan Luo , Junqi Jin , Han Zhu

Budgets play a significant role in real-world sequential auction markets such as those implemented by internet companies. To maximize the value provided to auction participants, spending is smoothed across auctions so budgets are used for…

Computer Science and Game Theory · Computer Science 2021-05-11 Vincent Conitzer , Christian Kroer , Eric Sodomka , Nicolas E. Stier-Moses

With a novel search algorithm or assortment planning or assortment optimization algorithm that takes into account a Bayesian approach to information updating and two-stage assortment optimization techniques, the current research provides a…

Theoretical Economics · Economics 2023-07-19 Dipankar Das

We derive optimal strategies for a bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We prove that, if everyone else bids locally in a single auction, the global bidder should always…

Computer Science and Game Theory · Computer Science 2014-01-16 Enrico H. Gerding , Rajdeep Kumar Dash , Andrew Byde , Nicholas Robert Jennings

The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated…

Machine Learning · Computer Science 2023-10-17 Dmytro Korenkevych , Frank Cheng , Artsiom Balakir , Alex Nikulkov , Lingnan Gao , Zhihao Cen , Zuobing Xu , Zheqing Zhu

Recent advances in machine learning have spurred significant interest in learning-augmented algorithms, particularly for online optimization. A growing body of work has studied online bidding in this framework, aiming to characterize the…

Data Structures and Algorithms · Computer Science 2026-05-11 Changyeol Lee , Dahoon Lee , Jongseo Lee , Yongho Shin , Changki Yun

Auction design for the modern advertising market has gained significant prominence in the field of game theory. With the recent rise of auto-bidding tools, an increasing number of advertisers in the market are utilizing these tools for…

Computer Science and Game Theory · Computer Science 2024-12-31 Changfeng Xu , Chao Peng , Chenyang Xu , Zhengfeng Yang

In display advertising, advertisers want to achieve a marketing objective with constraints on budget and cost-per-outcome. This is usually formulated as an optimization problem that maximizes the total utility under constraints. The…

Computer Science and Game Theory · Computer Science 2024-09-09 Anoop R Katti , Rui C. Gonçalves , Rinchin Iakovlev

Online advertising is a primary source of income for e-commerce platforms. In the current advertising pattern, the oriented targets are the online store owners who are willing to pay extra fees to enhance the position of their stores. On…

Computer Science and Game Theory · Computer Science 2024-08-20 Zhen Zhang , Weian Li , Yahui Lei , Bingzhe Wang , Zhicheng Zhang , Qi Qi , Qiang Liu , Xingxing Wang

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to…

Computer Science and Game Theory · Computer Science 2013-06-28 Shuai Yuan , Jun Wang , Xiaoxue Zhao

In this paper, we investigate the computation of second-price pacing equilibria (SPPEs), a foundational model in online advertising auctions. We present a polynomial-time algorithm for computing exact SPPEs in instances with a constant…

Computer Science and Game Theory · Computer Science 2026-05-12 Yiyang Huang , Yonglei Yan , Zihe Wang , Zhengyang Liu

Advertising becomes one of the most popular ways of monetizing an online transaction platform. Usually, sponsored advertisements are posted on the most attractive positions to enhance the number of clicks. However, multiple e-commerce…

Computer Science and Game Theory · Computer Science 2022-04-22 Weian Li , Qi Qi , Changjun Wang , Changyuan Yu