568 citations · 6.2k across the 157 of their papers we have counts for
159 papers
Fast Second-Order Online Kernel Learning through Incremental Matrix Sketching and Decomposition
Dongxie Wen, Xiao Zhang, Zhewei Wei +3
Online Kernel Learning (OKL) has attracted considerable research interest due to its promising predictive performance in streaming environments. Second-order approaches are particu…
Specify Robust Causal Representation from Mixed Observations
Mengyue Yang, Xinyu Cai, Furui Liu +2
Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hyp…
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing
Hangyu Wang, Ting Long, Liang Yin +6
Computerized Adaptive Testing(CAT) refers to an online system that adaptively selects the best-suited question for students with various abilities based on their historical respons…
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Jiarui Jin, Xianyu Chen, Weinan Zhang +5
Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existi…
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction
Jianghao Lin, Yanru Qu, Wei Guo +4
With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features…
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
Jianghao Lin, Rong Shan, Chenxu Zhu +6
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and…
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
Yuchen Fang, Zhenggang Tang, Kan Ren +7
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets. Recent advance…
Large Sequence Models for Sequential Decision-Making: A Survey
Muning Wen, Runji Lin, Hanjing Wang +6
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer visio…
Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation
Xianyu Chen, Jian Shen, Wei Xia +9
With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation s…
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +8
Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to…
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Jieming Zhu, Guohao Cai, Junjie Huang +3
Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackl…
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi +11
With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, con…
Order Matters: Agent-by-agent Policy Optimization
Xihuai Wang, Zheng Tian, Ziyu Wan +3
While multi-agent trust region algorithms have achieved great success empirically in solving coordination tasks, most of them, however, suffer from a non-stationarity problem since…
Refined Edge Usage of Graph Neural Networks for Edge Prediction
Jiarui Jin, Yangkun Wang, Weinan Zhang +5
Graph Neural Networks (GNNs), originally proposed for node classification, have also motivated many recent works on edge prediction (a.k.a., link prediction). However, existing met…
Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer
Hang Lai, Weinan Zhang, Xialin He +4
Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then trans…
A Bird's-eye View of Reranking: from List Level to Page Level
Yunjia Xi, Jianghao Lin, Weiwen Liu +5
Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface des…
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
Zhengbang Zhu, Rongjun Qin, Junjie Huang +4
Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingl…
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning
Jiarui Jin, Xianyu Chen, Weinan Zhang +5
Modelling the user's multiple behaviors is an essential part of modern e-commerce, whose widely adopted application is to jointly optimize click-through rate (CTR) and conversion r…
Forgetting Fast in Recommender Systems
Wenyan Liu, Juncheng Wan, Xiaoling Wang +3
Users of a recommender system may want part of their data being deleted, not only from the data repository but also from the underlying machine learning model, for privacy or utili…
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
Zeren Huang, Wenhao Chen, Weinan Zhang +7
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP branching data collected…
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation
Kounianhua Du, Weinan Zhang, Ruiwen Zhou +6
Prediction over tabular data is an essential and fundamental problem in many important downstream tasks. However, existing methods either take a data instance of the table independ…
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation
Bin Lu, Xiaoying Gan, Lina Yang +3
With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly foc…
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
Bin Lu, Xiaoying Gan, Weinan Zhang +3
Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, so…
A Graph-Enhanced Click Model for Web Search
Jianghao Lin, Weiwen Liu, Xinyi Dai +6
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models…