KDD

Pose Guided Fashion Image Synthesis Using Deep Generative Model

Wei Sun, Jawadul H. Bappy, Shanglin Yang, Yi Xu, Tianfu Wu, Hui Zhou

Comments KDD 2019 Workshop AI for Fashion

Empirical Study on Detecting Controversy in Social Media

Azadeh Nematzadeh, Grace Bang, Xiaomo Liu, Zhiqiang Ma

Comments The work is accepted by the 2nd KDD Workshop on Anomaly Detection in Finance, 2019. The authors contributed equally to this work, listed in the alphabetical order

Learning from Multi-User Activity Trails for B2B Ad Targeting

Shaunak Mishra, Jelena Gligorijevic, Narayan Bhamidipati

Comments 6 pages, accepted for AdKDD 2019 workshop held in conjunction with KDD 2019

Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, Jian Pei

Comments KDD 2018

Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification

Eitan Rothberg, Tingting Chen, Luo Jie, Hao Ji

Comments 4 pages (excluding references). Presented at AdvML: 1st Workshop on Adversarial Learning Methods for Machine Learning and Data Mining at KDD '19

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Mathias Kraus, Stefan Feuerriegel

Comments Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019)

Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior

Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Satish V. Ukkusuri

Comments Accepted in ACM KDD 2019

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Shunsuke Kitada, Hitoshi Iyatomi, Yoshifumi Seki

Comments 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) as an applied data science paper

Journal ref Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19), August 4--8, 2019, Anchorage, AK, USA

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi

Comments This paper has been accepted for publication at ACM KDD 2019

Machine Learning and the Internet of Things Enable Steam Flood Optimization for Improved Oil Production

Mi Yan, Jonathan C. MacDonald, Chris T. Reaume, Wesley Cobb, Tamas Toth, Sarah S. Karthigan

Comments Accepted by the 1st International Workshop on Artificial Intelligence of Things at KDD 2019

Journal ref The 1st International Workshop on Artificial Intelligence of Things at KDD 2019

Unsupervised Construction of Knowledge Graphs From Text and Code

Kun Cao, James Fairbanks

Comments 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 15th International Workshop On Mining and Learning with Graphs

Heterogeneous Relational Kernel Learning

Andre T. Nguyen, Edward Raff

Comments MileTS '19: 5th KDD Workshop on Mining and Learning from Time Series

A framework for anomaly detection using language modeling, and its applications to finance

Armineh Nourbakhsh, Grace Bang

Comments 5 pages, 2 figures, presented at the 2nd KDD Workshop on Anomaly Detection in Finance, 2019

Kernel Mean Embedding of Instance-wise Predictions in Multiple Instance Regression

Thomas Uriot

Comments KDD 2019, FEED Workshop

Trustable and Automated Machine Learning Running with Blockchain and Its Applications

Tao Wang, Xinmin Wu, Taiping He

Comments 10 pages, KDD 2019 AutoML workshop. arXiv admin note: text overlap with arXiv:1903.08801

Automatic Model Monitoring for Data Streams

Fábio Pinto, Marco O. P. Sampaio, Pedro Bizarro

Comments 9 pages, 9 figures, 2 tables

Journal ref KDD-ADF-2019

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh

Comments In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19)

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, Charles E. Leiserson

Comments 7 pages, Tutorial in the Anomaly Detection in Finance Workshop at the 25th SIGKDD Conference on Knowledge Discovery and Data Mining

Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval

John Brandt

Comments In FEED 19 Workshop at KDD 2019. Anchorage, AK, USA, 5 pages

Sparse hierarchical representation learning on molecular graphs

Matthias Bal, Hagen Triendl, Mariana Assmann, Michael Craig, Lawrence Phillips, Jarvist Moore Frost, Usman Bashir, Noor Shaker, Vid Stojevic

Comments 4 pages, 2 figures, accepted as a DLG 2019 workshop paper at KDD 2019

Learning a Unified Embedding for Visual Search at Pinterest

Andrew Zhai, Hao-Yu Wu, Eric Tzeng, Dong Huk Park, Charles Rosenberg

Comments in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge and Discovery and Data Mining, 2019

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Srijan Kumar, Xikun Zhang, Jure Leskovec

Comments ACM SIGKDD 2019 research track oral paper. The code and datasets are available on the project website: https://github.com/srijankr/jodie/ arXiv admin note: substantial text overlap with arXiv:1812.02289

The Efficacy of SHIELD under Different Threat Models

Cory Cornelius, Nilaksh Das, Shang-Tse Chen, Li Chen, Michael E. Kounavis, Duen Horng Chau

Comments Appraisal paper of existing method accepted for oral presentation at KDD LEMINCS 2019

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks

Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, Damian Borth

Comments 11 pages, 9 figures, 2nd KDD Workshop on Anomaly Detection in Finance, August 05, 2019, Anchorage, Alaska

MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, Sehee Chung

Comments Accepted as a full paper at KDD 2019

Chainer: A Deep Learning Framework for Accelerating the Research Cycle

Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent

Comments Accepted for Applied Data Science Track in KDD'19

KiloGrams: Very Large N-Grams for Malware Classification

Edward Raff, William Fleming, Richard Zak, Hyrum Anderson, Bill Finlayson, Charles Nicholas, Mark McLean

Comments Appearing in LEMINCS @ KDD'19, August 5th, 2019, Anchorage, Alaska, United States

Evaluation of Embeddings of Laboratory Test Codes for Patients at a Cancer Center

Lorenzo A. Rossi, Chad Shawber, Janet Munu, Finly Zachariah

Comments 2019 KDD Workshop on Applied Data Science for Healthcare (DSHealth, August 2019, Anchorage, AK). Make sure you have downloaded the latest version with the link to the DSHealth2019_loinc_embeddings GitHub repository: https://github.com/elleros/DSHealth2019_loinc_embeddings

Detecting Radical Text over Online Media using Deep Learning

Armaan Kaur, Jaspal Kaur Saini, Divya Bansal

Comments The Paper consists of 7 pages with 5 figures. The paper is accepted in Intelligent Information Feed Workshop of 25th ACM SIGKDD Conference 2019 for oral presentation

A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images

Tingting Zhao, Hang Zhang, Jacob Spoelstra

Comments 4 pages, 6 figures. To be presented at the 2019 KDD workshop on Applied data science in Healthcare: bridging the gap between data and knowledge