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Tentative schedule

Table of contents

  1. Week1 (-)
  2. Week2 (February 29)
  3. Week3 to Week5 (March 7-28)
  4. Week6 to Week7 (April 3-10)
  5. Week8 to Week9 (April 17-24)
  6. 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.,

Week6 to Week7 (April 3-10)

  • Presenter: Yongjian Zhong

Kumar, Ananya, et al. Verified Uncertainty Calibration. arXiv:1909.10155, arXiv, 31 Jan. 2020.,

Guo, Chuan, et al. On Calibration of Modern Neural Networks. arXiv:1706.04599, arXiv, 3 Aug. 2017.,

Popordanoska, Teodora, et al. A Consistent and Differentiable Lp Canonical Calibration Error Estimator. arXiv:2210.07810, arXiv, 13 Oct. 2022.,

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.,