SSN

Stochastic Second Order Methods under Interpolation (AISTATS 2020).

SSN [Paper].

1. Install requirements

Install the ssn optimizer.

pip install git+https://github.com/IssamLaradji/ssn.git

Install the Haven library for managing the experiments.

pip install -r requirements.txt

2. Usage

Use Ssn in your code by adding the following script.

import ssn
opt = ssn.Ssn(model.parameters())

for epoch in range(100):
    opt.zero_grad()
    closure = lambda : torch.nn.MSELoss() (model(X), Y)
    opt.step(closure=closure)

3. Experiments

Run an a synthetic experiment with the logistic loss with the command below,

python trainval.py -e syn_logistic -sb ../results -r 1

where -e is the experiment group, -sb is the result directory.

Other experiment groups are defined in exp_configs.py, which are the following:

• "syn_squared_hinge"
• "mushrooms_logistic"
• "mushrooms_squared_hinge"
• "ijcnn_logistic"
• "ijcnn_squared_hinge"
• "rcv1_logistic"
• "rcv1_squared_hinge"

Citation

@inproceedings{meng2020fast,
  title={Fast and furious convergence: Stochastic second order methods under interpolation},
  author={Meng, Si Yi and Vaswani, Sharan and Laradji, Issam Hadj and Schmidt, Mark and Lacoste-Julien, Simon},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1375--1386},
  year={2020}
}