DAIS Qual Reading List (Fall 2023)

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.    

Section 1

  1. 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
  2. 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

Section 2

  1. 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
  2. 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

Section 3

  • 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

Section 4

Section 5

Section 6

Section 7

Section 8

  • 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

Section 9

Section 10

Section 11

  • 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