Tentative schedule
Table of contents
- Week1 (-)
- Week2 (February 29)
- Week3 to Week5 (March 7-28)
- Week6 to Week7 (April 3-10)
- Week8 to Week9 (April 17-24)
- Week10 (May 1)
Week1 (-)
- Schedule meeting time
Week2 (February 29)
- Discuss which papers to read this semester
Week3 to Week5 (March 7-28)
- Presenter: Akash Choudhuri
Shafer, Glenn, and Vladimir Vovk. A Tutorial on Conformal Prediction. arXiv:0706.3188, arXiv, 21 June 2007. arXiv.org, https://doi.org/10.48550/arXiv.0706.3188.
Week6 to Week7 (April 3-10)
- Presenter: Yongjian Zhong
Kumar, Ananya, et al. Verified Uncertainty Calibration. arXiv:1909.10155, arXiv, 31 Jan. 2020. arXiv.org, https://doi.org/10.48550/arXiv.1909.10155.
Guo, Chuan, et al. On Calibration of Modern Neural Networks. arXiv:1706.04599, arXiv, 3 Aug. 2017. arXiv.org, https://doi.org/10.48550/arXiv.1706.04599.
Popordanoska, Teodora, et al. A Consistent and Differentiable Lp Canonical Calibration Error Estimator. arXiv:2210.07810, arXiv, 13 Oct. 2022. arXiv.org, https://doi.org/10.48550/arXiv.2210.07810.
Week8 to Week9 (April 17-24)
- Presenter: Hieu Vu
Austin, Jacob, et al. “Structured denoising diffusion models in discrete state-spaces.” Advances in Neural Information Processing Systems 34 (2021): 17981-17993.
Hoogeboom, Emiel, et al. “Autoregressive diffusion models.” arXiv preprint arXiv:2110.02037 (2021).
Kong, Lingkai, et al. “Autoregressive diffusion model for graph generation.” International conference on machine learning. PMLR, 2023.
Week10 (May 1)
- Presenter: Jeffrey Keithley
Elzayn, Hadi, et al. Fair Algorithms for Learning in Allocation Problems. arXiv:1808.10549, arXiv, 14 Nov. 2018. arXiv.org, https://doi.org/10.48550/arXiv.1808.10549.