3 citations · 5 across the 7 of their papers we have counts for
8 papers
Efficiency of Generalized Proportional First-Price Auctions Under Auto-bidding
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a retu…
One Model, Two Markets: Bid-Aware Generative Recommendation
Yanchen Jiang, Zhe Feng, Christopher P. Mah +2
Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However,…
Scaling Inference-Time Computation via Opponent Simulation: Enabling Online Strategic Adaptation in Repeated Negotiation
Xiangyu Liu, Di Wang, Zhe Feng +1
While large language models (LLMs) have emerged as powerful decision-makers across a wide range of single-agent and stationary environments, fewer efforts have been devoted to sett…
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…
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…
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers
Zhe Feng, Swati Padmanabhan, Di Wang
Online advertising has recently grown into a highly competitive and complex multi-billion-dollar industry, with advertisers bidding for ad slots at large scales and high frequencie…
Prior-Independent Auctions for Heterogeneous Bidders
Guru Guruganesh, Aranyak Mehta, Di Wang +1
We study the design of prior-independent auctions in a setting with heterogeneous bidders. In particular, we consider the setting of selling to bidders whose values are drawn f…
Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training
Goran Zuzic, Di Wang, Aranyak Mehta +1
We address the challenge of finding algorithms for online allocation (i.e. bipartite matching) using a machine learning approach. In this paper, we focus on the AdWords problem, wh…