The DAIS Qual Exam in Fall 2021 has been scheduled to be at 1-5pm on Monday, Oct. 4, 2021. 

This reading list consists of  multiple topic sections, 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. That is, if a section has two papers, you can expect to see one question related to the section in the qual exam, while if a section has three papers, you can expect to see two questions related to the section.

Section 1

• Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, and Kai Zheng. 2021. Learning to Ask Appropriate Questions in Conversational Recommendation. In Proceedings of ACM SIGIR 2021 808–817. DOI: https://doi.org/10.1145/3404835.3462839
• Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. Proceedings of ACM CIKM 2020, 275–284. DOI:  https://doi.org/10.1145/3340531.3411962
• Y. Song, Y. Tong, S. Bao, D. Jiang, H. Wu and R. C. -W. Wong, “TopicOcean: An Ever-Increasing Topic Model with Meta-Learning,” 2020 IEEE International Conference on Data Mining (ICDM), 2020, pp. 1262-1267, doi: 10.1109/ICDM50108.2020.00161.

Section 2

• Qinghai Zhou, Liangyue Li, Xintao Wu, Nan Cao, Lei Ying, Hanghang Tong: Attent: Active Attributed Network Alignment. WWW 2021: 3896-3906
• Si Zhang, Hanghang Tong, Long Jin, Yinglong Xia, Yunsong Guo: Balancing Consistency and Disparity in Network Alignment. KDD 2021: 2212-2222
• Jiaxuan You, Rex Ying, Jure Leskovec: Position-aware Graph Neural Networks. ICML 2019: 7134-714

Section 3

• J. Lee, Y. Bahri, R. Novak, S.S. Schoenholz, J. Pennington, J. Sohn-Dickstein, Deep Neural Networks as Gaussian Processes, ICLR 2018.
• L. Zheng, Y. Cheng, H. Yang, N. Cao, J. He. Deep Co-Attention Network for Multi-View Subspace Learning, WWW 2021.
• X. Chen, S. Wang, J. Wang, M. Long, Representation Subspace Distance for Domain Adaptation Regression, ICML 2021.

Section 4

• Jiaming Shen, Wenda Qiu, Yu Meng, Jingbo Shang, Xiang Ren and Jiawei Han, “TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names“, in Proc. 2021 Annual Conf. of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT’21), June 2021
• Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu, Heling Zhang, Zhaomeng Cheng and Jiawei Han, “BiTe-GCN: A New GCN Architecture via Bidirectional Convolution of Topology and Features on Text-Rich Networks“,  in Proc. 2021 ACM Int. Conf. on Web Search and Data Mining (WSDM’21), Feb. 2021
• Hongwei Wang, Hongyu Ren, Jure Leskovec, “Relational Message Passing for Knowledge Graph Completion“, Proc. 2021 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’21), Aug. 2021

Section 5

• Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh. 2020. AUTOPROMPT: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP2020).
• Sha Li, Heng Ji and Jiawei Han. 2021. Document-Level Event Argument Extraction by Conditional Generation. Proc. The 2021 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-HLT2021).
• Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. Proc. International Conference on Machine Learning (ICML2021).

Section 6

• Park, Yongjoo, Shucheng Zhong, and Barzan Mozafari. “Quicksel: Quick selectivity learning with mixture models.” Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2020.
• Van Aken, Dana, et al. “Automatic database management system tuning through large-scale machine learning.” Proceedings of the 2017 ACM International Conference on Management of Data. 2017.

Section 7

• X. Li, Q. Gu, Y. Zhu, T. Chen, and A. Banerjee,  Hessian based Analysis of SGD for Deep Nets: Dynamics and Generalization, SDM, 2020.
• Y. Zhou, Z. S. Wu, and A. Banerjee, Bypassing the Ambient Dimension: Previate SGD with Gradient Subspace Identification, ICLR, 2021.

Section 8

• Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram. Beyond localized graph neural networks: An attributed motif regularization framework. In 2020 IEEE International Conference on Data Mining (ICDM) (Virtual), pages 472–481, Sorrento, Italy, November 2020.
• Kanika Narang, Adit Krishnan, Junting Wang, Chaoqi Yang, Hari Sundaram, and Carolyn Sutter. A graph-convolutional ranking approach to leverage the relational aspects of user- generated content. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (virtual), 2021.

Section 9

• Gao, Junyi, Rakshith Sharma, Cheng Qian, Lucas M. Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, and Cao Xiao. 2021. “STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real-World Evidence.” Journal of the American Medical Informatics Association: JAMIA, January. https://doi.org/10.1093/jamia/ocaa322.
• Kargas, Nikos, Cheng Qian, Nicholas D. Sidiropoulos, Cao Xiao, Lucas M. Glass, and Jimeng Sun. 2020. “STELAR: Spatio-Temporal Tensor Factorization with Latent Epidemiological Regularization.” AAA’21. http://arxiv.org/abs/2012.04747.

Session 10

• Jie Huang, Kevin Chang, Jinjun Xiong, Wen-Mei Hwu: Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach. ACL/IJCNLP (1) 2021: 3641-3651.
• Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang: Geom-GCN: Geometric Graph Convolutional Networks. ICLR 2020.