2 citations · 4 across the 17 of their papers we have counts for
5 papers · 1 filter
OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios
Yewen Li, Peng Jiang, Yitian Li +3
Auto-bidding is central to computational advertising, where strategies must maximize advertisers' conversion value under economic constraints. It has evolved from rule-based contro…
C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding
Jinren Ding, Xuejian Xu, Shen Jiang +3
Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross-correlation mo…
Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration
Jingtong Gao, Ling Pan, Yejing Wang +6
Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optim…
Generative Auto-Bidding with Value-Guided Explorations
Jingtong Gao, Yewen Li, Shuai Mao +8
Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms.…
Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
Yi Zheng, Zehao Li, Peng Jiang +1
We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and th…