A group of students will read papers on interesting risk behaviors of prediction models in modern overparameterized regimes (e.g. deep learning).

• July 18 (M): Reconciling modern machine-learning practice and the classical bias–variance trade-off by Belkin et al.
• July 21 (R): Understanding Deep Learning (Still) Requires Rethinking Generalization by Zhang et al. (Discussion led by Lloyd Goldstein and Xuerong Wang)
  
• July 25 (M): The Implicit Bias of Gradient Descent on Separable Data by Soudry et al. (Discussion led by Arkobrato Gupta and Yumo Peng)
• July 28 (R): Two Models of Double Descent for Weak Features by Belkin et al. (Discussion led by Haotian Xie and Sayoni Roychowdhury)
• August 1 (M): How Many Variables Should be Entered in a Regression Equation? by Breiman and Freedman (Read and discussed by all)