第16届中国R会议暨2023X-AGI大会将于2023年11月25-30日在中国人民大学召开，本次会议由中国人民大学统计学院、中国人民大学应用统计科学研究中心、统计之都、原灵科技和中国商业统计学会人工智能分会（筹）主办，由中国人民大学统计学院数据科学与大数据统计系承办，得到宽德投资、明汯投资、和鲸科技、子博设计赞助支持，将以线上会议和线下会议相结合的方式举办。
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下面为您奉上本次中国R会议暨2023X-AGI大会数据科学理论专场演讲介绍， 本会场主席为吕晓玲。

数据科学理论专场

时间：2023年11月26日 上午9:30-11:30
会议地点：
  • 线下：中国人民大学立德楼807
  • 线上：点击阅读原文或扫描下方二维码

会场内容介绍

Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND
王菲菲
个人简介：
王菲菲，中国人民大学统计学院副教授，北京大学光华管理学院统计学博士。研究上关注文本挖掘及其商业应用、社交网络分析、大数据建模等，研究论文发表于Journal of Econometric, Journal of Business and Econometric Statistics, Journal of Machine Learning Research, 中国科学（数学）等国内外高水平期刊上。主持并参与了国家自科基金项目、教育部社科重大项目、国家重点研发项目等多个课题。曾获中国人民大学优秀科研成果奖、课外教学优秀奖等。
报告摘要：
There is an emerging trend in the Chinese automobile industries that automakers are introducing exclusive enterprise social networks (EESNs) to expand sales and provide after-sale services. The traditional online social networks (OSNs) and enterprise social networks (ESNs), such as Twitter and Yammer, are ingeniously designed to facilitate unregulated communications among equal individuals. However, users in EESNs are naturally social stratified, consisting of both enterprise staffs and customers. In addition, the motivation to operate EESNs can be quite complicated, including providing customer services and facilitating communication among enterprise staffs. As a result, the social behaviors in EESNs can be quite different from those in OSNs and ESNs. In this work, we aim to analyze the characteristics of social patterns in EESNs and study the driving forces of social link formation by formulating it as a link prediction problem in heterogeneous social networks. In a typical EESN provided by the Chinese car manufacturer NIO, we derive plentiful user features, build multiple meta-path graphs, and develop a novel Fast (H)eterogeneous graph (A)ttention (N)etwork algorithm for (D)irected graphs (FastHAND) to predict directed social links among users. The algorithm introduces feature group attentions in node-level and uses edge sampling algorithm over directed meta-path graphs to reduce the computation cost. Experimental results demonstrate the predictive power of our proposed method and our intuitions about social affinity propagation in EESNs.
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
周峰
个人简介：
周峰，中国人民大学统计学院讲师，中国人民大学杰出青年学者。主要从事统计机器学习、贝叶斯方法、随机过程等。现已在Journal of Machine Learning Research、Statistics and Computing、Machine Learning、ICLR、NeurIPS等期刊会议发表论文20余篇。主持国家自然科学基金青年项目，中国博士后基金特别资助、面上资助，入选博士后国际交流计划引进项目，中国人民大学科研国际合作支持项目星火计划。
报告摘要：
In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP is an enhanced version of Deep Mixture Point Processes (DMPP), which uses a more flexible deep kernel to model complex relationships between events and covariate data, improving the model's expressiveness. To address the intractable training procedure of DKMPP due to the non-integrable deep kernel, we utilize an integration-free method based on score matching, and further improve efficiency by adopting a scalable denoising score matching method. Our experiments demonstrate that DKMPP and its corresponding score-based estimators outperform baseline models, showcasing the advantages of incorporating covariate information, utilizing a deep kernel, and employing score-based estimators.
Trustworthy Policy Learning under the Counterfactual No-Harm Criterion
刘越
个人简介：
刘越，中国人民大学讲师，2019年博士毕业于北京大学。多篇文章发表于Journal of Machine Learning Research（JMLR）, Artificial Intelligence（AIJ）,IEEE Transactions on Knowledge and Data Engineering（TKDE）, IEEE Transactions on Neural Networks and Learning Systems（TNNLS）， SIGKDD, International Conference on Machine Learning（ICML），The Conference on Uncertainty in Artificial Intelligence（UAI）等机器学习与统计学期刊及会议。研究兴趣主要包括因果推断，贝叶斯网络以及基于因果推断的机器学习算法等。
报告摘要：
Trustworthy policy learning has significant importance in making reliable and harmless treatment decisions for individuals. Previous policy learning approaches aim at the well-being of subgroups by maximizing the utility function (e.g., conditional average causal effects, post-view click-through\&conversion rate in recommendations), however, individual-level counterfactual no-harm criterion has rarely been discussed. In this paper, we first formalize the counterfactual no-harm criterion for policy learning from a principal stratification perspective. Next, we propose a novel upper bound for the fraction negatively affected by the policy and show the consistency and asymptotic normality of the estimator. Based on the estimators for the policy utility and harm upper bounds, we further propose a policy learning approach that satisfies the counterfactual no-harm criterion, and prove its consistency to the optimal policy reward for parametric and non-parametric policy classes, respectively. Extensive experiments are conducted to show the effectiveness of the proposed policy learning approach for satisfying the counterfactual no-harm criterion.
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
蔡智博
个人简介：
蔡智博，男，现任中国人民大学统计学院数据科学与大数据统计系讲师。主要研究兴趣包括充分降维、变量选择及其在机器学习中的应用等。学术论文在JASA，ICLR，NeurIPS等期刊会议上发表。
报告摘要：
Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevailing in semi-supervised top-K recommendations. The basic idea is to routinely conduct heuristic-based data augmentation and apply contrastive losses (e.g. InfoNCE) on the augmented views. Yet, some CF-tailored challenges make this adoption suboptimal, such as the issue of out-of-distribution, the risk of false negatives, and the nature of top-K evaluation. They necessitate the CL-based CF scheme to focus more on mining hard negatives and distinguishing false negatives from the vast unlabeled user-item interactions, for informative contrast signals. To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods. AdvInfoNCE adaptively explores and assigns hardness to each negative instance in an adversarial fashion and further utilizes a fine-grained hardness-aware ranking criterion to empower the recommender's generalization ability. Training CF models with AdvInfoNCE, we validate the effectiveness of AdvInfoNCE on both synthetic and real-world benchmark datasets, thus showing its generalization ability to mitigate out-of-distribution problems. 

线下参与

本会场将线上线下同步进行，线下会场位于中国人民大学，线上会场为学说直播平台。线下参会者需要扫描下方二维码报名。欢迎各位线上线下的朋友共同参会！

关于会议

主办方：
  • 中国人民大学统计学院
  • 中国人民大学应用统计科学研究中心
  • 统计之都
  • 原灵科技
  • 中国商业统计学会人工智能分会（筹）
赞助方：
  • 宽德投资
  • 明汯投资

  • 和鲸科技
  • 子博设计

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