What are methods to make a predictive model more robust to outliers?

Outliers Handling:

1. Remove Outliers as many as possible. (Using smoothing method, moving average/ Savizy-Golay Method)
2. Use the robust algorithms: Tree Algorithms (decision tree)/ Regularization(SVM, slack variable )
   Regression base model is more senstive to outliers.
3. Use different metric (example: median instead of mean, absolute deviation instead of standard deviation): this method is mostly applied when outliers probably distort the distribution.
4. some are resistant to outliers: tree, random forest, clustering, k-nearest neighbors.