Peng Wu - RankMe

44 papers A* 23Journal 19Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Talos: Optimizing Top-K Accuracy in Recommender Systems. WWW Shengjia Zhang, Weiqin Yang, Jiawei Chen, Peng Wu, Yuegang Sun, Gang Wang, Qihao Shi, Can Wang
2026	J	`jnl`	Talos: Optimizing Top-K Accuracy in Recommender Systems. CoRR Shengjia Zhang, Weiqin Yang, Jiawei Chen, Peng Wu, Yuegang Sun, Gang Wang, Qihao Shi, Can Wang
2025	—	`conf`	Breaking the Top- KDD (2) Weiqin Yang, Jiawei Chen, Shengjia Zhang, Peng Wu, Yuegang Sun, Yan Feng, Chun Chen, Can Wang
2025	J	`jnl`	Breaking the Top- CoRR Weiqin Yang, Jiawei Chen, Shengjia Zhang, Peng Wu, Yuegang Sun, Yan Feng, Chun Chen, Can Wang
2025	J	`jnl`	Confounded Causal Imitation Learning with Instrumental Variables. CoRR Yan Zeng, Shenglan Nie, Feng Xie, Libo Huang, Peng Wu, Zhi Geng
2025	J	`jnl`	Identification and Estimation of Long-Term Treatment Effects with Monotone Missing. CoRR Qinwei Yang, Ruocheng Guo, Shasha Han, Peng Wu
2025	J	`jnl`	Learning Counterfactual Outcomes Under Rank Preservation. CoRR Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo, Kun Zhang
2025	A*	`conf`	Optimal Policy Adaptation Under Covariate Shift. IJCAI Xueqing Liu, Qinwei Yang, Zhaoqing Tian, Ruocheng Guo, Peng Wu
2025	J	`jnl`	Optimal Policy Adaptation under Covariate Shift. CoRR Xueqing Liu, Qinwei Yang, Zhaoqing Tian, Ruocheng Guo, Peng Wu
2025	J	`jnl`	Proactive Recommendation in Social Networks: Steering user interest with causal inference. AI Open Hang Pan, Shuxian Bi, Wenjie Wang, Haoxuan Li, Peng Wu, Fuli Feng
2024	A*	`conf`	Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference. ICLR Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He
2024	J	`jnl`	Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference. CoRR Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He
2024	A*	`conf`	Debiased Collaborative Filtering with Kernel-Based Causal Balancing. ICLR Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui
2024	J	`jnl`	Debiased Collaborative Filtering with Kernel-Based Causal Balancing. CoRR Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui
2024	A*	`conf`	Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards. NeurIPS Qinwei Yang, Xueqing Liu, Yan Zeng, Ruocheng Guo, Yang Liu, Peng Wu
2024	A*	`conf`	Local Causal Structure Learning in the Presence of Latent Variables. ICML Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
2024	J	`jnl`	Local Causal Structure Learning in the Presence of Latent Variables. CoRR Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
2024	A*	`conf`	Policy Learning for Balancing Short-Term and Long-Term Rewards. ICML Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng
2024	J	`jnl`	Policy Learning for Balancing Short-Term and Long-Term Rewards. CoRR Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng
2024	J	`jnl`	Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence. CoRR Hang Pan, Shuxian Bi, Wenjie Wang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He
2024	A*	`conf`	Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering. ICML Haoxuan Li, Chunyuan Zheng, Shuyi Wang, Kunhan Wu, Eric Hao Wang, Peng Wu, Zhi Geng, Xu Chen, Xiao-Hua Zhou
2024	A*	`conf`	Treatment Effect Estimation for User Interest Exploration on Recommender Systems. SIGIR Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua
2024	J	`jnl`	Treatment Effect Estimation for User Interest Exploration on Recommender Systems. CoRR Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua
2024	A*	`conf`	Uncovering the Propensity Identification Problem in Debiased Recommendations. ICDE Honglei Zhang, Shuyi Wang, Haoxuan Li, Chunyuan Zheng, Xu Chen, Li Liu, Shanshan Luo, Peng Wu
2023	A*	`conf`	Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations. WWW Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu
2023	J	`jnl`	Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations. CoRR Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu
2023	A*	`conf`	Causal Recommendation: Progresses and Future Directions. SIGIR Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He
2023	A*	`conf`	Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach. NeurIPS Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu, Xiangnan He
2023	A*	`conf`	Multiple Robust Learning for Recommendation. AAAI Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Xiao-Hua Zhou, Peng Wu
2023	A*	`conf`	Propensity Matters: Measuring and Enhancing Balancing for Recommendation. ICML Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Peng Cui
2023	A*	`conf`	Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach. NeurIPS Haoxuan Li, Kunhan Wu, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Zhi Geng, Fuli Feng, Xiangnan He, Peng Wu
2023	A*	`conf`	StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. ICLR Haoxuan Li, Chunyuan Zheng, Peng Wu
2023	A*	`conf`	TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. ICLR Haoxuan Li, Yan Lyu, Chunyuan Zheng, Peng Wu
2023	A*	`conf`	Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. ICML Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu, Peng Wu
2023	A*	`conf`	Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation. KDD Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui
2022	A*	`conf`	A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. KDD Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Rui Zhang, Jie Sun
2022	J	`jnl`	A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. CoRR Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Rui Zhang, Jie Sun
2022	J	`jnl`	A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems. CoRR Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, Yuhao Deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou
2022	—	`conf`	Accepted Tutorials at The Web Conference 2022. WWW (Companion Volume) Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lyu, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He
2022	A*	`conf`	Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. KDD Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, Yongdong Zhang
2022	J	`jnl`	Causal Analysis Framework for Recommendation. CoRR Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou
2022	J	`jnl`	Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random. CoRR Peng Wu, Haoxuan Li, Yan Lyu, Xiao-Hua Zhou
2022	A*	`conf`	On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. IJCAI Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou
2022	J	`jnl`	Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. CoRR Haoxuan Li, Chunyuan Zheng, Xiao-Hua Zhou, Peng Wu