DAIS Qual Exam Reading List (Fall 2022)

The DAIS Qual Exam in Fall 2022 (written exam) has been scheduled to be at 1-5pm on Monday, Oct. 3, 2022 in Siebel Center room 3401.

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 usually expect to see one question related to the section in the qual exam, while if a section has three papers, you can usually expect to see two questions related to the section.

Section 1

  • Maria Maistro, Lucas Chaves Lima, Jakob Grue Simonsen, and Christina Lioma. 2021. Principled Multi-Aspect Evaluation Measures of Rankings. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, New York, NY, USA, 1232–1242. DOI:https://doi.org/10.1145/3459637.3482287 PDF file: http://www.library.illinois.edu/proxy/go.php?url=https://dl.acm.org/doi/pdf/10.1145/3459637.3482287
  • Naseri S., Dalton J., Yates A., Allan J. (2021) CEQE: Contextualized Embeddings for Query Expansion. In: Hiemstra D., Moens MF., Mothe J., Perego R., Potthast M., Sebastiani F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science, vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_31  PDF file:  https://link.springer.com/content/pdf/10.1007%2F978-3-030-72113-8_31.pdf
  • Salle A., Malmasi S., Rokhlenko O., Agichtein E. (2021) Studying the Effectiveness of Conversational Search Refinement Through User Simulation. In: Hiemstra D., Moens MF., Mothe J., Perego R., Potthast M., Sebastiani F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science, vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_39. PDF file: https://link.springer.com/content/pdf/10.1007%2F978-3-030-72113-8_39.pdf

Section 2

Section 3

  • Jan Overgoor, Austin Benson, and Johan Ugander. Choosing to grow a graph: modeling network formation as discrete choice. In The World Wide Web Conference, pages 1409–1420. ACM, 2019. https://arxiv.org/abs/1811.05008
  • Yuxin Xiao, Adit Krishnan, and Hari Sundaram. Discovering strategic behaviors for collaborative content-production in social networks. In The Web Conference (WebConf 2020), pages 2078–2088, Taipei, Taiwan, April 2020.  https://arxiv.org/abs/2003.03670
  • Harshay Shah, Suhansanu Kumar, and Hari Sundaram. Growing attributed networks through local processes. In The World Wide Web Conference – WWW ’19, pages 3208–3214. ACM Press, May 2019.   https://arxiv.org/abs/1712.10195

Section 4

  • Tamari, Ronen and Shani, Chen and Hope, Tom and Petruck, Miriam R L and Abend, Omri and Shahaf, Dafna. 2020. {L}anguage (Re)modelling: {T}owards Embodied Language Understanding. ACL2020. https://aclanthology.org/2020.acl-main.559
  • Kolluru, Keshav and Mohammed, Muqeeth and Mittal, Shubham and Chakrabarti, Soumen and Mausam. 2022. Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction. ACL2022. https://aclanthology.org/2022.acl-long.179
  • Sundriyal, Megha and Malhotra, Ganeshan and Akhtar, Md Shad and Sengupta, Shubhashis and Fano, Andrew and Chakraborty, Tanmoy. 2022. Document Retrieval and Claim Verification to Mitigate {COVID}-19 Misinformation. ACL2022. https://aclanthology.org/2022.constraint-1.8

Section 5

Section 6

  • Fu, Tianfan, Cao Xiao, Cheng Qian, Lucas M. Glass, and Jimeng Sun. 2021. “Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery.” In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 404–14. KDD ’21.
  • Fu, Tianfan, Kexin Huang, Cao Xiao, Lucas M. Glass, and Jimeng Sun. 2022. “HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data.” Cell Patterns and also at arXiv [cs.CY]. arXiv. http://arxiv.org/abs/2102.04252 .
  • Huang, Kexin, Cao Xiao, Lucas M. Glass, and Jimeng Sun. 2020. “MolTrans: Molecular Interaction Transformer for Drug–target Interaction Prediction.” Bioinformatics , October. https://doi.org/10.1093/bioinformatics/btaa880.

Section 7

Section 8

  • Y. Zhou, X. Li, and A. Banerjee, “Noisy Truncated SGD: Optimization and Generalization,” SIAM International Conference on Data Mining (SDM), 2022, https://arxiv.org/abs/2103.00075
  • M. Belkin, D. Hsu, S. Ma, S. Mandal, “Reconciling modern machine learning practice and the bias-variance trade-off,” Proceedings National Academy of Science (PNAS), 2019, https://www.pnas.org/content/116/32/15849.short

Section 9

Session 10