Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/14326#discussion_r71992373
 Diff: mllib/src/main/scala/org/apache/spark/ml/regression/RobustRegression.scala 
@@ 0,0 +1,466 @@
+/*
+ * 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/LICENSE2.0
+ *
+ * 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.regression
+
+import scala.collection.mutable
+
+import breeze.linalg.{DenseVector => BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, LBFGSB
=> BreezeLBFGSB}
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.BLAS._
+import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util._
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Dataset, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Params for robust regression.
+ */
+private[regression] trait RobustRegressionParams extends PredictorParams with HasRegParam
+ with HasMaxIter with HasTol with HasFitIntercept with HasStandardization with HasWeightCol
{
+
+ /**
+ * The shape parameter to control the amount of robustness. Must be > 1.0.
+ * At larger values of M, the huber criterion becomes more similar to least squares
regression;
+ * for small values of M, the criterion is more similar to L1 regression.
+ * Default is 1.35 to get as much robustness as possible while retaining
+ * 95% statistical efficiency for normally distributed data.
+ */
+ @Since("2.1.0")
+ final val m = new DoubleParam(this, "m", "The shape parameter to control the amount
of " +
+ "robustness. Must be > 1.0.", ParamValidators.gt(1.0))
+
+ /** @group getParam */
+ @Since("2.1.0")
+ def getM: Double = $(m)
+}
+
+/**
+ * Robust regression.
+ *
+ * The learning objective is to minimize the huber loss, with regularization.
+ *
+ * The robust regression optimizes the squared loss for the samples where
+ * {{{ \frac{(y  X \beta)}{\sigma}\leq M }}}
+ * and the absolute loss for the samples where
+ * {{{ \frac{(y  X \beta)}{\sigma}\geq M }}},
+ * where \beta and \sigma are parameters to be optimized.
+ *
+ * This supports two types of regularization: None and L2.
+ *
+ * This estimator is different from the R implementation of Robust Regression
+ * ([[http://www.ats.ucla.edu/stat/r/dae/rreg.htm]]) because the R implementation does
a
+ * weighted least squares implementation with weights given to each sample on the basis
+ * of how much the residual is greater than a certain threshold.
+ */
+@Since("2.1.0")
+class RobustRegression @Since("2.1.0") (@Since("2.1.0") override val uid: String)
+ extends Regressor[Vector, RobustRegression, RobustRegressionModel]
+ with RobustRegressionParams with Logging {
+
+ @Since("2.1.0")
 End diff 
I don't think you need `@Since` at every symbol in the class (that was `@Since` itself
with the same annotation).

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