Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/19020#discussion_r139764922
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/HuberAggregator.scala
---
@@ -0,0 +1,142 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.optim.aggregator
+
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.Vector
+
+/**
+ * HuberAggregator computes the gradient and loss for a huber loss
function,
+ * as used in robust regression for samples in sparse or dense vector in
an online fashion.
+ *
+ * The huber loss function based on:
+ * Art B. Owen (2006), A robust hybrid of lasso and ridge regression.
+ * (http://statweb.stanford.edu/~owen/reports/hhu.pdf)
+ *
+ * Two HuberAggregator can be merged together to have a summary of loss
and gradient of
+ * the corresponding joint dataset.
+ *
+ * The huber loss function is given by
+ *
+ * <blockquote>
+ * $$
+ * \begin{align}
+ * \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma +
+ * H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) +
\frac{1}{2}\alpha {||w||_2}^2}
+ * \end{align}
+ * $$
+ * </blockquote>
+ *
+ * where
+ *
+ * <blockquote>
+ * $$
+ * \begin{align}
+ * H_m(z) = \begin{cases}
+ * z^2, & \text {if } |z| < \epsilon, \\
+ * 2\epsilon|z| - \epsilon^2, & \text{otherwise}
+ * \end{cases}
+ * \end{align}
+ * $$
+ * </blockquote>
+ *
+ * It is advised to set the parameter $\epsilon$ to 1.35 to achieve 95%
statistical efficiency.
--- End diff --
This description is misleadingly general since this claim only applies to
normally distributed data. How about referencing the part of the paper which
talks about this so that people can look up what is meant here?
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