• Batch normalization
  • Makes the objective function and gradients smoother (more Lipschitz) (Santurkar 2018 - How Does Batch Normalization Help Optimization?. See also section 3 of De 2020.)
• Weight normalization
  • Improves the conditioning of the optimization problem (Salimans & Kingma 2016)

• Choromanska et al 2014 - The Loss Surfaces of Multilayer Networks
• Du et al 2018 - Gradient Descent Provably Optimizes Over-parameterized Neural Networks “We show that as long as m is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function, for an m hidden node shallow neural network with ReLU activation and n training data.”
• Goldblum et al 2020 - Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory Examines optimization issues in deep learning and their relation to ML theory

• Yao et al 2019 - PYHESSIAN: Neural Networks Through the Lens of the Hessian

If the weights are unbounded, the Lipschitz constant will be unbouned (true even for logistic regression).

• 2019 - Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
• 2018 - Lipschitz regularity of deep neural networks: analysis and efficient estimation

• Bengio et al 2013 - Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation Introduces straight-through estimator
• REINFORCE
• Peng et al 2018 - Backpropagating through Structured Argmax using a SPIGOT