This reading list consists of  6 sections, with each containing 2-3 papers.  The questions in the written exam will be based on the papers listed here, with 1-2 questions related to each section.

Section 1

Dawei Zhou, Lecheng Zheng, Jiawei Han, Jingrui He: A Data-Driven Graph Generative Model for Temporal Interaction Networks. KDD 2020: 401-411
Dongruo Zhou, Lihong Li, Quanquan Gu: Neural Contextual Bandits with UCB-based Exploration. ICML 2020: 11492-11502
Honglei Zhuan, Chi Wang, Yifan Wang: Identifying Outlier Arms in Multi-Armed Bandit. NIPS 2017: 5204-5213

Section 2

Xinya Du and Claire Cardie. 2020. Event Extraction by Answering (Almost) Natural Questions. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 671–683.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

Section 3

Guansong Pang, Chunhua Shen, Anton van den Hengel: Deep Anomaly Detection with Deviation Networks. KDD 2019: 353-362
Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong: InFoRM: Individual Fairness on Graph Mining. KDD 2020: 379-389
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, Suresh Venkatasubramanian: Certifying and Removing Disparate Impact. KDD 2015: 259-268

Section 4

Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang and Jiawei Han, “Text Classification Using Label Names Only: A Language Model Self-Training Approach“, in Proc. 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP’20), Nov. 2020
Yifei Ma, Balakrishnan (Murali) Narayanaswamy, Haibin Lin and Hao Ding, “Temporal-Contextual Recommendation in Real-Time“, KDD 2020

Section 5

Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020. Controlling Fairness and Bias in Dynamic Learning-to-Rank. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 429–438. http://www.library.illinois.edu/proxy/go.php?url=https://doi.org/10.1145/3397271.3401100
Burges, Christopher JC. “From ranknet to lambdarank to lambdamart: An overview.” Learning 11, no. 23-581 (2010): 81.  https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.180.634&rep=rep1&type=pdf

Section 6

Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang: Curvature Regularization to Prevent Distortion in Graph Embedding. NeurIPS 2020
Wanyun Cui, Yanghua Xiao, Haixun Wang, Yangqiu Song, Seung-won Hwang, Wei Wang: KBQA: Learning Question Answering over QA Corpora and Knowledge Bases. PVLDB 10(5): 565-576, 2017. VLDB17

Section 7

Kraska, Tim, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. The case for learned index structures. In Proceedings of the 2018 International Conference on Management of Data, pp. 489-504. 2018.
Hilprecht, Benjamin, Carsten Binnig, and Uwe Röhm. Learning a partitioning advisor for cloud databases. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 143-157. 2020.