Difference between revisions of "Seminar Spring 2024"

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Latest revision as of 15:01, 20 August 2024

  • Theme: AI for Health
  • Instructor: Prof. Chenyang Lu
  • Semester: Spring 2024
  • Time: Wednesday at 1 PM
  • Location: McKelvey 1030
  • Guidelines:
  • Recommended sources: NeurIPS ([1]), ICML ([2]), AAAI ([3]), SIGKDD ([4]), IJCAI ([5]), IMWUT ([6]), HEALTH ([7]), Digital Medicine ([8]), Lancet Digital Health ([9]), NEJM AI ([10])

Presentation Schedule

---Jan 24 ---

Hangyue

Krishnamachari, Kiran, See-Kiong Ng, and Chuan-Sheng Foo. "Mitigating Real-World Distribution Shifts in the Fourier Domain." Transactions on Machine Learning Research (2023). [11]

---Jan 31 ---

Ruiqi

Faure, Gueter Josmy, Min-Hung Chen, and Shang-Hong Lai. "Holistic interaction transformer network for action detection." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3340-3350. 2023. [12]

---Feb 07 ---

Jizhou

Parikh, Harsh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, and Alexander Volfovsky. "Estimating Trustworthy and Safe Optimal Treatment Regimes." arXiv preprint arXiv:2310.15333 (2023). [13]

---Feb 14 ---

Ben

M. Wornow, R. Thapa, E. Steinberg, J. A. Fries, and N. H. Shah, “EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models.” arXiv, Dec. 11, 2023. doi: 10.48550/arXiv.2307.02028.

L. L. Guo et al., “A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records.” arXiv, Nov. 19, 2023. Available: http://arxiv.org/abs/2311.11483

---Feb 21 ---

Quan

Maus, N., Jones, H., Moore, J., Kusner, M. J., Bradshaw, J., & Gardner, J. (2022). Local latent space bayesian optimization over structured inputs. Advances in Neural Information Processing Systems, 35, 34505-34518. [14]

---Feb 28 ---

Ziqi

Oral exam practice

[1] Udandarao, Vishaal, et al. “COBRA: Contrastive Bi-Modal Representation Algorithm”, IJCAI (TUSION workshop) 2020. [15]

[2] Han Zongbo, et al. “Trusted Multi-View Classification.” International Conference on Learning Representations (ICLR). 2020. [16]

[3] Zhenbang Wu et al. “Multimodal Patient Representation Learning with Missing Modalities and Labels”. International Conference on Learning Representations(ICLR), 2024. [17]

---Mar 06 ---

Jiaming

Zhang, Nan, Yusen Zhang, Wu Guo, Prasenjit Mitra, and Rui Zhang. "FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 10915-10931. 2023. [18]

---Mar 13 ---

Spring Break

---Mar 20 ---

Charles

Van Veen, Dave, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek et al. "Adapted large language models can outperform medical experts in clinical text summarization." Nature Medicine (2024): 1-9. [19]

Liu, Nelson F., Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. "Lost in the Middle: How Language Models Use Long Contexts." Transactions of the Association for Computational Linguistics 12 (2024). [20]

---Mar 27 ---

Jingwen

Gouareb, Racha, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, and Douglas Teodoro. "Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study." Health Data Science 3 (2023): 0099. [21]

---Apr 03 ---

Hanyang

Jin, Ming, et al. "Time-llm: Time series forecasting by reprogramming large language models." ICLR 2024.

---Apr 10 ---

Zebo

Lockwood, Owen, and Mei Si. "Reinforcement learning with quantum variational circuit." In Proceedings of the AAAI conference on artificial intelligence and interactive digital entertainment, vol. 16, no. 1, pp. 245-251. 2020. [22]

---Apr 17 ---

Claire

Prerna Chikersal, Afsaneh Doryab, Michael Tumminia, Daniella K. Villalba, Janine M. Dutcher, Xinwen Liu, Sheldon Cohen, Kasey G. Creswell, Jennifer Mankoff, J. David Creswell, Mayank Goel, and Anind K. Dey. 2021. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Trans. Comput.-Hum. Interact. 28, 1, Article 3 (February 2021), 41 pages. [23]

---Apr 24 ---

Daoyi

Tipirneni, Sindhu, and Chandan K. Reddy. "Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series." ACM Transactions on Knowledge Discovery from Data (TKDD) 16, no. 6 (2022): 1-17. [24]

Previous Semesters

Previous Lab Meetings

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