Tutorials

  • CIKM 2021 Tutorial on Fairness of Machine Learning in Recommender Systems ‐ Yunqi Li (Rutgers University, USA), Yingqiang Ge (Rutgers University, USA), Yongfeng Zhang (Rutgers University, USA)
  • AutoML: From Methodology to Application ‐ Yaliang Li (Alibaba Group, USA), Zhen Wang (Alibaba Group, China), Yuexiang Xie (Alibaba Group, China), Bolin Ding (Alibaba Group, USA), Kai Zeng (Alibaba Group, China), Ce Zhang (ETH Z├╝rich, Switzerland)
  • Fake News, Disinformation, Propaganda, and Media Bias ‐ Preslav Nakov (Qatar Computing Research Institute, HBKU, Qatar), Giovanni Da San Martino (University of Padova, Italy)
  • Large-Scale Information Extraction under Privacy-Aware Constraints ‐ Rajeev Gupta (MICROSOFT, India), Ranganath Kondapally (MICROSOFT, India)
  • IR From Bag-of-words to BERT and Beyond through Practical Experiments ‐ Craig Macdonald (University of Glasgow, United Kingdom), Nicola Tonellotto (University of Pisa, Italy), Sean MacAvaney (University of Glasgow, United Kingdom)
  • Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications ‐ Wenjie Ruan (University of Exeter, United Kingdom), Xinping Yi (University of Liverpool, United Kingdom), Xiaowei Huang (University of Liverpool, United Kingdom)
  • Fair Graph Mining ‐ Jian Kang (University of Illinois at Urbana-Champaign, USA), Hanghang Tong (University of Illinois at Urbana-Champaign, USA)
  • Online Advertising Incrementality Testing: Practical Lessons And Emerging Challenges ‐ Joel Barajas (Yahoo Research, Verizon Media, USA), Narayan Bhamidipati (Yahoo Research, Verizon Media, USA), James G. Shanahan (Church and Duncan Group Inc and UC Berkeley, USA)
  • Aggregation Techniques in Crowdsourcing: Multiple Choice Questions and Beyond ‐ Djellel Difallah (NYU Abu Dhabi, UAE), Alessandro Checco (The University of Sheffield, United Kingdom)