3 citations · 7 across the 12 of their papers we have counts for
13 papers
Generative Auto-Bidding with Unified Modeling and Exploration
Mingming Zhang, Feiqing Zhuang, Na Li +7
Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Ma…
Cross-Domain Offline Policy Adaptation via Selective Transition Correction
Mengbei Yan, Jiafei Lyu, Shengjie Sun +5
It remains a critical challenge to adapt policies across domains with mismatched dynamics in reinforcement learning (RL). In this paper, we study cross-domain offline RL, where an…
Towards Multi-modal Multi-turn Safety: From Agentic Interaction to Strategic Alignment
Han Zhu, Jiale Chen, Chengkun Cai +8
Despite remarkable capability in multi-modal understanding, deploying Multi-modal Large Language Models (MLLMs) in open-ended conversational scenarios introduces safety risks that…
Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies
Mingming Zhang, Na Li, Zhuang Feiqing +8
With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches fo…
PROF: An LLM-based Reward Code Preference Optimization Framework for Offline Imitation Learning
Shengjie Sun, Jiafei Lyu, Runze Liu +4
Offline imitation learning (offline IL) enables training effective policies without requiring explicit reward annotations. Recent approaches attempt to estimate rewards for unlabel…
Delta Decompression for MoE-based LLMs Compression
Hao Gu, Wei Li, Lujun Li +5
Mixture-of-Experts (MoE) architectures in large language models (LLMs) achieve exceptional performance, but face prohibitive storage and memory requirements. To address these chall…
VLP: Vision-Language Preference Learning for Embodied Manipulation
Runze Liu, Chenjia Bai, Jiafei Lyu +3
Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is…
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning
Shengjie Sun, Runze Liu, Jiafei Lyu +3
Large Language Models (LLMs) have shown significant potential in designing reward functions for Reinforcement Learning (RL) tasks. However, obtaining high-quality reward code often…
Cross-channel Budget Coordination for Online Advertising System
Guangyuan Shen, Shenjie Sun, Dehong Gao +4
In online advertising (Ad), advertisers are always eager to know how to globally optimize their budget allocation strategies across different channels for more conversions such as…
EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising
Guangyuan Shen, Shengjie Sun, Dehong Gao +3
We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertisi…
Time-aware Graph Embedding: A temporal smoothness and task-oriented approach
Yonghui Xu, Shengjie Sun, Yuan Miao +6
Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, mos…
Optimal Delivery with Budget Constraint in E-Commerce Advertising
Chao Wei, Weiru Zhang, Shengjie Sun +4
Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search a…
Important Attribute Identification in Knowledge Graph
Shengjie Sun, Dong Yang, Hongchun Zhang +4
The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various na…