12 citations · 22 across the 16 of their papers we have counts for
17 papers
PACE: Progressive Angular-to-Norm Contrastive Embedding
Yanping Li, Wei Zhou, Yawen Liu +7
Multimodal embedding models encode heterogeneous inputs into a shared embedding space, enabling efficient similarity computation across modalities and tasks. Most existing methods…
M: Dual-Memory Augmentation for Long-Horizon Web Agents via Trajectory Summarization and Insight Retrieval
Dawei Yan, Haokui Zhang, Guangda Huzhang +8
Multimodal Large Language Models (MLLMs) based agents have demonstrated remarkable potential in autonomous web navigation. However, handling long-horizon tasks remains a critical b…
Building Autonomous GUI Navigation via Agentic-Q Estimation and Step-Wise Policy Optimization
Yibo Wang, Guangda Huzhang, Yuwei Hu +7
Recent advances in Multimodal Large Language Models (MLLMs) have substantially driven the progress of autonomous agents for Graphical User Interface (GUI). Nevertheless, in real-wo…
Optimal Transport-Based Token Weighting scheme for Enhanced Preference Optimization
Meng Li, Guangda Huzhang, Haibo Zhang +2
Direct Preference Optimization (DPO) has emerged as a promising framework for aligning Large Language Models (LLMs) with human preferences by directly optimizing the log-likelihood…
Establishing Reliability Metrics for Reward Models in Large Language Models
Yizhou Chen, Yawen Liu, Xuesi Wang +5
The reward model (RM) that represents human preferences plays a crucial role in optimizing the outputs of large language models (LLMs), e.g., through reinforcement learning from hu…
Residual Multi-Task Learner for Applied Ranking
Cong Fu, Kun Wang, Jiahua Wu +5
Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their…
Recurrent Temporal Revision Graph Networks
Yizhou Chen, Anxiang Zeng, Guangda Huzhang +6
Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for tempor…
Clustered Embedding Learning for Recommender Systems
Yizhou Chen, Guangda Huzhang, Anxiang Zeng +7
In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector f…
Exploit Customer Life-time Value with Memoryless Experiments
Zizhao Zhang, Yifei Zhao, Guangda Huzhang
As a measure of the long-term contribution produced by customers in a service or product relationship, life-time value, or LTV, can more comprehensively find the optimal strategy f…
A General Traffic Shaping Protocol in E-Commerce
Chenlin Shen, Guangda Huzhang, Yuhang Zhou +2
To approach different business objectives, online traffic shaping algorithms aim at improving exposures of a target set of items, such as boosting the growth of new commodities. Ge…
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System
Qi Hao, Tianze Luo, Guangda Huzhang
The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles. Existing algorithms u…
Imitate TheWorld: A Search Engine Simulation Platform
Yongqing Gao, Guangda Huzhang, Weijie Shen +4
Recent E-commerce applications benefit from the growth of deep learning techniques. However, we notice that many works attempt to maximize business objectives by closely matching o…
Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce
Xuesi Wang, Guangda Huzhang, Qianying Lin +1
Ensemble models in E-commerce combine predictions from multiple sub-models for ranking and revenue improvement. Industrial ensemble models are typically deep neural networks, follo…
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao +8
Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collecte…
Truthful Fair Division without Free Disposal
Xiaohui Bei, Guangda Huzhang, Warut Suksompong
We study the problem of fairly dividing a heterogeneous resource, commonly known as cake cutting and chore division, in the presence of strategic agents. While a number of results…
Lines, betweenness and metric spaces
Pierre Aboulker, Xiaomin Chen, Guangda Huzhang +2
A classic theorem of Euclidean geometry asserts that any noncollinear set of points in the plane determines at least distinct lines. Chen and Chvátal conjectured that this…
Graph metric with no proper inclusion between lines
Xiaomin Chen, Guangda Huzhang, Peihan Miao +1
In trying to generalize the classic Sylvester-Gallai theorem and De Bruijn-Erdős theorem in plane geometry, lines and closure lines were previously defined for metric spaces and hy…