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Robust HSIC

This repository aims to replicate the results from the ICML2020 paper "Robust Learning with the Hilbert-Schmidt Independence Criterion"

Results

Synthetic Data

Training Params:

  • Batch Size: 32
  • Learning Rate: 1e-3
  • L2 Regularization (Weight Decay): 1e-3
  • Num Epochs (Best of): 10

Experiment Params:

  • Loss Function: HSIC
  • Num. Trials: 20

Dataset Size vs. MSE graph with Gaussian Noise

Dataset Size vs. MSE graph with Shifted Exponential Noise

Dataset Size vs. MSE graph with Laplacian Noise

Rotated MNIST

Training Params:

  • Batch Size: 32
  • Learning Rate: 1e-3
  • L2 Regularization (Weight Decay): None
  • Num Epochs (Best of): 7

HSIC and Cross Entropy vs. Accuracy