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20232026
most citedAuto-bidding and Auctions in Online Advertising: A Survey

1 citations · 2 across the 16 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024

Linear Transformer Topological Masking with Graph Random Features

Isaac Reid, Kumar Avinava Dubey, Deepali Jain +12

When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relativ…

cs.GT2024★ 1 cited

Auto-bidding and Auctions in Online Advertising: A Survey

Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23

In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…

cs.GT2024

Deep Reinforcement Learning for Sequential Combinatorial Auctions

Sai Srivatsa Ravindranath, Zhe Feng, Di Wang +3

Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and stron…

cs.LG2024

Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning

Kaiwen Wang, Rahul Kidambi, Ryan Sullivan +17

Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models tha…

cs.DS2024

The Average-Value Allocation Problem

Kshipra Bhawalkar, Zhe Feng, Anupam Gupta +3

We initiate the study of centralized algorithms for welfare-maximizing allocation of goods to buyers subject to average-value constraints. We show that this problem is NP-hard to a…

cs.GT2024

Auctions with LLM Summaries

Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi +2

We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM), e.g., an ad auction in which the display…

econ.TH2024

Ads in Conversations

Martino Banchio, Aranyak Mehta, Andres Perlroth

We study the optimal placement of advertisements for interactive platforms like conversational AI assistants. Importantly, conversations add a feature absent in canonical search ma…