Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining最新文献

Identifiability of Cause and Effect using Regularized Regression 用正则化回归分析因果关系的可辨识性

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330854

Alexander Marx, Jilles Vreeken

引用次数: 21

Integrating Domain-Knowledge into Deep Learning 将领域知识集成到深度学习中

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3340416

R. Salakhutdinov

引用次数: 8

Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach 基于双管齐下方法的动态图快速准确异常检测

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330946

Minji Yoon, Bryan Hooi, Kijung Shin, C. Faloutsos

{"title":"Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach","authors":"Minji Yoon, Bryan Hooi, Kijung Shin, C. Faloutsos","doi":"10.1145/3292500.3330946","DOIUrl":"https://doi.org/10.1145/3292500.3330946","url":null,"abstract":"Given a dynamic graph stream, how can we detect the sudden appearance of anomalous patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can we categorize the types of anomalies that occur in practice, and theoretically analyze the anomalous signs arising from each type? In this work, we propose AnomRank, an online algorithm for anomaly detection in dynamic graphs. AnomRank uses a two-pronged approach defining two novel metrics for anomalousness. Each metric tracks the derivatives of its own version of a 'node score' (or node importance) function. This allows us to detect sudden changes in the importance of any node. We show theoretically and experimentally that the two-pronged approach successfully detects two common types of anomalies: sudden weight changes along an edge, and sudden structural changes to the graph. AnomRank is (a) Fast and Accurate: up to 49.5x faster or 35% more accurate than state-of-the-art methods, (b) Scalable: linear in the number of edges in the input graph, processing millions of edges within 2 seconds on a stock laptop/desktop, and (c) Theoretically Sound: providing theoretical guarantees of the two-pronged approach.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116660609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54

Riker 瑞克

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330985

Jie Zhao, Ziyu Guan, Huan Sun

{"title":"Riker","authors":"Jie Zhao, Ziyu Guan, Huan Sun","doi":"10.1145/3292500.3330985","DOIUrl":"https://doi.org/10.1145/3292500.3330985","url":null,"abstract":"This work studies product question answering (PQA) which aims to answer product-related questions based on customer reviews. Most recent PQA approaches adopt end2end semantic matching methodologies, which map questions and answers to a latent vector space to measure their relevance. Such methods often achieve superior performance but it tends to be difficult to interpret why. On the other hand, simple keyword-based search methods exhibit natural interpretability through matched keywords, but often suffer from the lexical gap problem. In this work, we develop a new PQA framework (named Riker) that enjoys the benefits of both interpretability and effectiveness. Riker mines rich keyword representations of a question with two major components, internal word re-weighting and external word association, which predict the importance of each question word and associate the question with outside relevant keywords respectively, and can be jointly trained under weak supervision with large-scale QA pairs. The keyword representations from Riker can be directly used as input to a keyword-based search module, enabling the whole process to be effective while preserving good interpretability. We conduct extensive experiments using Amazon QA and review datasets from 5 different departments, and our results show that Riker substantially outperforms previous state-of-the-art methods in both synthetic settings and real user evaluations. In addition, we compare keyword representations from Riker and those from attention mechanisms popularly used for deep neural networks through case studies, showing that the former are more effective and interpretable.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"3 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120986416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Naranjo Question Answering using End-to-End Multi-task Learning Model 使用端到端多任务学习模型的纳兰霍问答

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330770

Bhanu Pratap Singh Rawat, Fei Li, Hong Yu

引用次数: 7

Investigate Transitions into Drug Addiction through Text Mining of Reddit Data 通过对Reddit数据的文本挖掘来研究药物成瘾的转变

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330737

John Lu, S. Sridhar, Ritika Pandey, M. Hasan, G. Mohler

{"title":"Investigate Transitions into Drug Addiction through Text Mining of Reddit Data","authors":"John Lu, S. Sridhar, Ritika Pandey, M. Hasan, G. Mohler","doi":"10.1145/3292500.3330737","DOIUrl":"https://doi.org/10.1145/3292500.3330737","url":null,"abstract":"Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtained data from Reddit, an online collection of forums, to gather insight into drug use/misuse using text snippets from users narratives. Specifically, using users' posts, we trained a binary classifier which predicts a user's transitions from casual drug discussion forums to drug recovery forums. We also proposed a Cox regression model that outputs likelihoods of such transitions. In doing so, we found that utterances of select drugs and certain linguistic features contained in one's posts can help predict these transitions. Using unfiltered drug-related posts, our research delineates drugs that are associated with higher rates of transitions from recreational drug discussion to support/recovery discussion, offers insight into modern drug culture, and provides tools with potential applications in combating the opioid crisis.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 第25届ACM SIGKDD知识发现与数据挖掘国际会议论文集

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500

引用次数: 69

Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning 零阶优化及其在数据挖掘和机器学习中的对抗鲁棒性应用研究进展

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3332288

Pin-Yu Chen, Sijia Liu

{"title":"Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning","authors":"Pin-Yu Chen, Sijia Liu","doi":"10.1145/3292500.3332288","DOIUrl":"https://doi.org/10.1145/3292500.3332288","url":null,"abstract":"Zeroth-order (ZO) optimization is increasingly embraced for solving big data and machine learning problems when explicit expressions of the gradients are difficult or infeasible to obtain. It achieves gradient-free optimization by approximating the full gradient via efficient gradient estimators. Some recent important applications include: a) generation of prediction-evasive, black-box adversarial attacks on deep neural networks, b) online network management with limited computation capacity, c) parameter inference of black-box/complex systems, and d) bandit optimization in which a player receives partial feedback in terms of loss function values revealed by her adversary. This tutorial aims to provide a comprehensive introduction to recent advances in ZO optimization methods in both theory and applications. On the theory side, we will cover convergence rate and iteration complexity analysis of ZO algorithms and make comparisons to their first-order counterparts. On the application side, we will highlight one appealing application of ZO optimization to studying the robustness of deep neural networks - practical and efficient adversarial attacks that generate adversarial examples from a black-box machine learning model. We will also summarize potential research directions regarding ZO optimization, big data challenges and some open-ended data mining and machine learning problems.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Applications of AI/ML in Established and New Industries AI/ML在现有和新兴行业中的应用

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3340403

H. Sawaf

引用次数: 0

Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning 解决增量半监督支持向量学习的平衡约束问题

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-25 DOI: 10.1145/3292500.3330962

Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, J. Pei, Heng Huang

{"title":"Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning","authors":"Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, J. Pei, Heng Huang","doi":"10.1145/3292500.3330962","DOIUrl":"https://doi.org/10.1145/3292500.3330962","url":null,"abstract":"Semi-Supervised Support Vector Machine (S3VM) is one of the most popular methods for semi-supervised learning. To avoid the trivial solution of classifying all the unlabeled examples to a same class, balancing constraint is often used with S3VM (denoted as BCS3VM). Recently, a novel incremental learning algorithm (IL-S3VM) based on the path following technique was proposed to significantly scale up S3VM. However, the dynamic relationship of balancing constraint with previous labeled and unlabeled samples impede their incremental method for handling BCS3VM. To fill this gap, in this paper, we propose a new incremental S3VM algorithm (IL-BCS3VM) based on IL-S3VM which can effectively handle the balancing constraint and directly update the solution of BCS3VM. Specifically, to handle the dynamic relationship of balancing constraint with previous labeled and unlabeled samples, we design two unique procedures which can respectively eliminate and add the balancing constraint into S3VM. More importantly, we provide the finite convergence analysis for our IL-BCS3VM algorithm. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-BCS3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"26 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114032800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9