25 citations · 67 across the 9 of their papers we have counts for
10 papers
LACRM: LLM-Guided Mining of Directed Advertiser Competition Relationships in E-Commerce Advertising
Xiang Shi, Yitian Kou, Bing Li +6
In e-commerce advertising, ad platforms typically fail to model the externalities among advertisers when making optimization decisions: an advertiser's actual performance is largel…
LangBP: Language-Guided Reasoning and Acting for Joint Bidding and Pricing
Jiaqi Ding, Chuan Yang, Linghui Meng +5
Auto-bidding is a long-horizon sequential decision problem for maximizing conversion value under budget and key performance indicator (KPI) constraints. Recent work extends this ta…
JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing
Linghui Meng, Chun Gan, Shengsheng Niu +8
Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget. Howeve…
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
Xiyun Li, Ziyi Ni, Jingqing Ruan +4
Adaptive human-agent and agent-agent cooperation are becoming more and more critical in the research area of multi-agent reinforcement learning (MARL), where remarked progress has…
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition
Ziyi Ni, Minglun Han, Feilong Chen +4
Enhancing automatic speech recognition (ASR) performance by leveraging additional multimodal information has shown promising results in previous studies. However, most of these wor…
GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning
Jingqing Ruan, Yali Du, Xuantang Xiong +6
Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximi…
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
Linghui Meng, Muning Wen, Yaodong Yang +7
Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also d…
Learning Multi-agent Action Coordination via Electing First-move Agent
Jingqing Ruan, Linghui Meng, Xuantang Xiong +2
Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously…
Settling the Variance of Multi-Agent Policy Gradients
Jakub Grudzien Kuba, Muning Wen, Yaodong Yang +5
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL…
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition
Linghui Meng, Jin Xu, Xu Tan +3
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking…