Author: keshav4@illinois.edu

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 

Spring 2023

The written exam of the DAIS Qual Exam in Spring 2023 will be held on Monday, Feb. 20, 2023, at 1pm-5pm in room 2407 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 Zhang, Yunyi Zhang, Martin Michalski, Yucheng Jiang, Yu Meng, and Jiawei Han, “Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts”, in Proc. 2023 ACM Int. Conf. on Web Search and Data Mining (WSDM’23), Feb. 2023
  2. Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji and Jiawei Han, “Open-Vocabulary Argument Role Prediction for Event Extraction”, in Proc. 2022 Conf. on Empirical Methods in Natural Language Processing (EMNLP’22), Dec. 2022
  3. 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

Section 2

  1. Chen, C., Sun, F., Zhang, M., and Ding, B. Recommendation unlearning. In Proceedings of the ACM Web Conference 2022 (New York, NY, USA, 2022), WWW ’22, Association for Computing Machinery, pp. 2768–2777. (https://dl.acm.org/doi/10.1145/3485447.3511997) 
  2. Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., and Zhang, Y. Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (New York, NY, USA, 2021), SIGIR ’21, Association for Computing Machinery, pp. 11–20. (https://dl.acm.org/doi/10.1145/3404835.3462875) 
  3. Karimi, A.-H., Schölkopf, B., and Valera, I. Algorithmic recourse: From counterfactual explanations to interventions. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (New York, NY, USA, 2021), FAccT ’21, Association for Computing Machinery, pp. 353–362. (https://dl.acm.org/doi/10.1145/3442188.3445899) 

Section 3

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

  • Zhe Xu, Boxin Du, Hanghang Tong: Graph Sanitation with Application to Node Classification. WWW 2022: 1136-1147. (https://arxiv.org/abs/2105.09384) 
  • Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. In SIGKDD. ACM, 66–74. (https://arxiv.org/abs/2005.10203)

Section 10

  • 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 ]
  • Fontana, M., Iori, M., Montobbio, F., and Sinatra, R. (2020) New and atypical combinations: An assessment of novelty and interdisciplinarity (2020) Research Policy, 2020, vol. 49, issue 7 https://doi.org/10.1016/j.respol.2020.104063 [relevant preceding papers are (i) https://doi.org/10.1162/qss_a_00007 (ii) 10.1126/science.12404]

Section 11

  • Wang, B., Wang, X., Tao, T., Zhang, Q., & Xu, J. (2020). Neural Question Generation with Answer Pivot. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9138-9145.PDF file: https://ojs.aaai.org/index.php/AAAI/article/view/6449
  • 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
  • Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS 2020 PDF file: https://arxiv.org/abs/2005.11401

Section 12

  • 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
  • A. Banerjee, T. Chen, X. Li, Y. Zhou, Stability Based Generalization Bounds for Exponential Family Langevin Dynamics, International Conference on Machine Learning (ICML), 2022. https://arxiv.org/abs/2201.03064

Fall 2022

The written exam of the DAIS Qual Exam in Fall 2022 will be held on Monday, Oct. 3, 2022, at 1pm-5pm 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

Section 2

Section 3

  • Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464):447–453, 2019. (DOI: 10.1126/science.aax2342)
  • Rediet Abebe, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, and David G. Robinson. Roles for computing in social change. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20, pages 252–260, New York, NY, USA, 2020. Association for Computing Machinery. (https://dl.acm.org/doi/abs/10.1145/3351095.3372871) 

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. https://dl.acm.org/doi/pdf/10.1145/3447548.3467286
  • 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

  • Chockchowwat, Supawit, Chaitanya Sood, and Yongjoo Park. “Airphant: Cloud-oriented Document Indexing.” 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022. https://arxiv.org/pdf/2112.13323.pdf
  • Chockchowwat, Supawit, Wenjie Liu, and Yongjoo Park. “Automatically Finding Optimal Index Structure.” arXiv preprint arXiv:2208.03823 (2022). https://arxiv.org/pdf/2208.03823.pdf

Section 8

Section 9

Session 10

Spring 2022

The DAIS Qual Exam in Spring 2022 has been scheduled to be at 1-5pm on Monday, Feb. 28, 2022. 

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

  • 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

  • Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han and Martin Burke. 2022. Chemical-Reaction-aware Molecule Representation Learning. Proc. The International Conference on Learning Representations (ICLR2022). https://blender.cs.illinois.edu/paper/moleculerepresentation2021a.pdf
  • Yiwei Luo, Dallas Card and Dan Jurafsky. 2020. Detecting Stance in Media on Global Warming. Proc. The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP2020). https://aclanthology.org/2020.findings-emnlp.296v3.pdf
  • Yi R. Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal and Avi Sil. 2021. InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection. Proc. The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021). https://blender.cs.illinois.edu/paper/infosurgeon2021.pdf

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

Fall 2021

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

Section 2

Section 3

Section 4

Section 5

Section 6

Section 7

Section 8

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

Spring 2021

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

Section 2

Section 3

Section 4

Section 5

Section 6

Section 7

Fall 2020

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

Section 2

Section 3

Section 4

Section 5

Section 6

Spring 2020

Data Mining

  • Basic Concepts
    • Data mining: frequent pattern mining (be able to list half a dozen typical methods), sequential pattern mining (be able to list at four or five typical methods), correlation analysis, classification (be able to list half a dozen typical methods), clustering (be able to  list half a dozen typical methods)
    • Basic machine learning and deep learning concepts
    • Data warehousing: star schema, data cube (be able to list half a dozen typical data cube computation methods), multi-dimensional analysis (OLAP)
  • Background
    • J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 2nd edition. Chapters 2-10, Morgan Kaufmann 2011.
  • More advanced topics
    • Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R Voss, Jiawei Han, “Automated Phrase Mining from Massive Text Corpora”, IEEE Transactions on Knowledge and Data Engineering, 30(10):1825-1837 (2018)
    • Fangbo Tao, Chao Zhang, Xiusi Chen, Meng Jiang, Tim Hanratty, Lance Kaplan, and Jiawei Han, “Doc2Cube: Allocating Documents to Text Cube without Labeled Data“, in Proc of 2018 IEEE Int. Conf. on Data Mining (ICDM’18), Singapore, Nov. 2018
    • Leman Akoglu, Hanghang Tong, Danai Koutra: Graph based anomaly detection and description: a survey. Data Min. Knowl. Discov. 29(3): 626-688 (2015)
    • Chen Chen, Ruiyue Peng, Lei Ying, Hanghang Tong: Network Connectivity Optimization: Fundamental Limits and Effective Algorithms. KDD 2018: 1167-1176
    • Harshay Shah, Suhansanu Kumar, and Hari Sundaram, Growing attributed networks through local processes, The World Wide Web Conference – WWW ’19, ACM Press, May 2019, pp. 3208–3214. https://doi.org/10.1145/3308558.3313640

Database Management Systems

  • Basic concepts
    • Hardware: disk sector, track, block, seek, latency, how to lay out a database page
    • Data modeling: ER, OO, and Object-Relational approaches
    • Concurrency control and recovery: ACID, serializability, two-phase locking, two-phase commit, logging and recovery, the impact of data replication
    • Theory: normalization, dependencies
    • Queries: access methods (hashing, B-trees, multidimensional access methods), how to optimize a query, SQL
    • Benchmarks: TPC-C and TPC-H, OLAP, Data Cubes
  • Background
    • You can use any database textbook you like to study the most basic of the concepts listed above; for example, CS411 teaches these concepts. (Note that you will be expected to be able to demonstrate your understanding of the concepts by applying them (as opposed to simply being able to define them).) A good reference for the background content is the “Database Systems” textbook by Garcia-Molina, Widom, and Ullman. In addition, we would like you to study the following papers:
      • E. F. Codd: A Relational Model of Data for Large Shared Data Banks. CACM 13(6): 377-387. 1970
      • Architecture of a DBMS. Stonebraker et al. Foundations and Trends in Databases, 2007
  • More advanced topics
    Please note that databases are a very broad field. The papers listed here will be changed frequently, to reflect this breadth
    • Antonin Guttman: R-Trees: A Dynamic Index Structure for Spatial Searching. SIGMOD Conference 1984: 47-57. PDF
    • Mausam, Michael Schmitz, Robert Bart, Stephen Soderland, and Oren Etzioni. “Open Language Learning for Information Extraction.” In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 523–534. EMNLP-CoNLL ’12
    •  M. Zhou, H. Wang, and K. C.-C. Chang: Learning to Rank from Distant Supervision: Exploiting Noisy Redundancy for Relational Entity Search. In ICDE 2013, pages 829-840, 2013
    • 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

Information Retrieval

  • Basic concepts
    • Vector-space retrieval model, TF-IDF weighting, relevance/pseudo feedback, query expansion, mean average precision (MAP), normalized discounted cumulative gain (NDCG), query-likelihood retrieval model, language model smoothing, PageRank, inverted index, probabilistic topic model (i.e., Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation), learning to rank.
  • Background
  • More advanced topics
    • Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, and Hongning Wang. 2019. Variance Reduction in Gradient Exploration for Online Learning to Rank. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). ACM, New York, NY, USA, 835-844. URL: http://www.library.illinois.edu/proxy/go.php?url=https://doi.org/10.1145/3331184.3331264
    •   Tetsuya Sakai and Zhaohao Zeng. 2019. Which Diversity Evaluation Measures Are “Good”?. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). ACM, New York, NY, USA, 595-604. URL: http://www.library.illinois.edu/proxy/go.php?url=https://doi.org/10.1145/3331184.3331215.
    • Rolf Jagerman, Harrie Oosterhuis, and Maarten de Rijke. 2019. To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). ACM, New York, NY, USA, 15-24. DOI: https://doi-org.proxy2.library.illinois.edu/10.1145/3331184.3331269
    • Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019. Transparent, Scrutable and Explainable User Models for Personalized Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). ACM, New York, NY, USA, 265-274. DOI: https://doi-org.proxy2.library.illinois.edu/10.1145/3331184.3331211
Data and Intelligent Systems
Email: yongjoo@g.illinois.edu