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
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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.