The written exam of the DAIS Qual Exam in Fall 2023 will be held on Friday, Oct. 6, 2023, at 1pm-5pm in room 2124 Siebel Center.
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. 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. You only need to answer four of those questions in the exam, so there is no need for you to read every paper. Instead, it would make sense for you to browse through the list and identify 8~10 papers that you are most familiar with or most comfortable with reading, and then focus on reading/digesting those papers. In general, you will likely find some sections to be closer to your interests or background than others, and you can focus more on reading the papers in those a few sections that seem to be closest to your research interests.
- Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han, “Generating Training Data with Language Models: Towards Zero-Shot Language Understanding“, in Proc. 2022 Conf. on Neural Information Processing Systems (NeurIPS’22), Nov. 2022
- Bowen Jin, Yu Zhang, Qi Zhu, Jiawei Han, “Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks”, in Proc. 2023 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’23), Long Beach, CA, August 2023
- Perdomo,J.,Zrnic,T.,Mendler-Du ̈nner, C.,and Hardt,M.(2020).Performative prediction. In III, H. D. and Singh, A., editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 7599–7609. PMLR. https://proceedings.mlr.press/v119/perdomo20a.html
- Singh, D. D., Das, S., and Chakraborty, A. (2023). Fairassign: Stochastically fair driver assignment in gig delivery platforms. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’23, pages 753–763, New York, NY, USA. Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3593013.3594040
- Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi.2022. BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. Proc. CVPR2022. https://proceedings.mlr.press/v162/li22n.html
- Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke Miller and Maddie Simens and Amanda Askell and Peter Welinder and Paul Christiano and Jan Leike and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. Proc. NeurIPS2022. https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf
- Vladika, Juraj and Matthes, Florian. 2023. Scientific Fact-Checking: A Survey of Resources and Approaches. Proc. ACL2023 Findings. https://arxiv.org/abs/2305.16859
- Chockchowwat, Supawit, Wenjie Liu, and Yongjoo Park. “AirIndex: Versatile Index Tuning Through Data and Storage.” SIGMOD’24. https://arxiv.org/pdf/2306.14395.pdf
- Zhaoheng Li , Pranav Gor , Rahul Prabhu , Hui Yu , Yuzhou Mao, Yongjoo Park. “ElasticNotebook: Enabling Live Migration for Computational Notebooks.” PVLDB’23. https://billyzhaohengli.github.io/files/Elastic_Notebook_Class_Presentation.pdf
- Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. Conformal Prediction with Temporal Quantile Adjustments. NeurIPS 2022
- Yan, Jiahuan, Jintai Chen, Yixuan Wu, Danny Z. Chen, and Jian Wu. 2023. “T2G-FORMER: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction.” AAAI’23
- Daniel Kang, John Guibas, Peter D. Bailis, Tatsunori Hashimoto, Matei Zaharia. TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data. SIGMOD 2022
- Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia. Model Assertions for Monitoring and Improving ML Models. MLSys 2020.
- Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen: Beyond Low-frequency Information in Graph Convolutional Networks. AAAI 2021 (https://arxiv.org/abs/2101.00797)
- Zhe Xu, Yuzhong Chen, Qinghai Zhou, Yuhang Wu, Menghai Pan, Hao Yang, Hanghang Tong: Node Classification Beyond Homophily: Towards a General Solution. KDD 2023: 2862-2873 (https://dl.acm.org/doi/10.1145/3580305.3599446)
- Chen Xu, Piji Li, Wei Wang, Haoran Yang, Siyun Wang, and Chuangbai Xiao. 2022. COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 201–211. https://www.library.illinois.edu/proxy/go.php?url=https://doi.org/10.1145/3477495.3531957
- Wenqiang Lei, Yao Zhang, Feifan Song, Hongru Liang, Jiaxin Mao, Jiancheng Lv, Zhenglu Yang, and Tat-Seng Chua. 2022. Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 212–222.https://www.library.illinois.edu/proxy/go.php?url=https://doi.org/10.1145/3477495.3532001
- Cheng, M., Smith, D. S., Ren, X., Cao, H., Smith, S., & McFarland, D. A. (2023). How New Ideas Diffuse in Science. American Sociological Review 88 (3), 522-561, https://journals-sagepub-com.proxy2.library.illinois.edu/doi/10.1177/00031224231166955
- Park, M., Leahey, E. & Funk, R.J. (2023) Papers and patents are becoming less disruptive over time. Nature 613, 138–144 . https://doi.org/10.1038/s41586-022-05543-x [relevant preceding paper DOI: 10.1038/s41586-019-0941-9 ]
- DEER: Descriptive Knowledge Graph for Explaining Entity Relationships. Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu. In The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. PDF file: https://arxiv.org/abs/2205.10479
- A. J. Stewart, C. Robinson, I. A. Corley, A. Ortiz, J. M. Lavista Ferres, and A. Banerjee, TorchGeo: Deep Learning with Geospatial Data, ACM SIGSPATIAL International Conference in Geographic Information Systems (SIGSPATIAL), 2022. https://arxiv.org/pdf/2111.08872.pdf
- A. Banerjee, P. Cisneros-Velarde, L. Zhu, and M. Belkin, Restricted Strong Convexity of Deep Learning Models with Smooth Activations, International Conference on Learning Representations (ICLR), 2023. https://openreview.net/pdf?id=PINRbk7h01