diff --git a/src/Microsoft.ML.AutoML/API/AutoCatalog.cs b/src/Microsoft.ML.AutoML/API/AutoCatalog.cs
index 280777db70..a965fcf832 100644
--- a/src/Microsoft.ML.AutoML/API/AutoCatalog.cs
+++ b/src/Microsoft.ML.AutoML/API/AutoCatalog.cs
@@ -10,6 +10,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Runtime;
 using Microsoft.ML.SearchSpace;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Trainers.FastTree;
 
 namespace Microsoft.ML.AutoML
@@ -313,22 +314,37 @@ public AutoMLExperiment CreateExperiment(AutoMLExperiment.AutoMLExperimentSettin
         /// <param name="useFastForest">true if use fast forest as available trainer.</param>
         /// <param name="useLgbm">true if use lgbm as available trainer.</param>
         /// <param name="useFastTree">true if use fast tree as available trainer.</param>
-        /// <param name="useLbfgs">true if use lbfgs as available trainer.</param>
-        /// <param name="useSdca">true if use sdca as available trainer.</param>
+        /// <param name="useLbfgsLogisticRegression">true if use <see cref="LbfgsLogisticRegressionBinaryTrainer"/> as available trainer.</param>
+        /// <param name="useSdcaLogisticRegression">true if use <see cref="SdcaLogisticRegressionBinaryTrainer"/> as available trainer.</param>
         /// <param name="fastTreeOption">if provided, use it as initial option for fast tree, otherwise the default option will be used.</param>
         /// <param name="lgbmOption">if provided, use it as initial option for lgbm, otherwise the default option will be used.</param>
         /// <param name="fastForestOption">if provided, use it as initial option for fast forest, otherwise the default option will be used.</param>
-        /// <param name="lbfgsOption">if provided, use it as initial option for lbfgs, otherwise the default option will be used.</param>
-        /// <param name="sdcaOption">if provided, use it as initial option for sdca, otherwise the default option will be used.</param>
+        /// <param name="lbfgsLogisticRegressionOption">if provided, use it as initial option for <paramref name="lbfgsLogisticRegressionSearchSpace"/>, otherwise the default option will be used.</param>
+        /// <param name="sdcaLogisticRegressionOption">if provided, use it as initial option for <paramref name="sdcaLogisticRegressionSearchSpace"/>, otherwise the default option will be used.</param>
         /// <param name="fastTreeSearchSpace">if provided, use it as search space for fast tree, otherwise the default search space will be used.</param>
         /// <param name="lgbmSearchSpace">if provided, use it as search space for lgbm, otherwise the default search space will be used.</param>
         /// <param name="fastForestSearchSpace">if provided, use it as search space for fast forest, otherwise the default search space will be used.</param>
-        /// <param name="lbfgsSearchSpace">if provided, use it as search space for lbfgs, otherwise the default search space will be used.</param>
-        /// <param name="sdcaSearchSpace">if provided, use it as search space for sdca, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsLogisticRegressionSearchSpace">if provided, use it as search space for <see cref="LbfgsLogisticRegressionBinaryTrainer"/>, otherwise the default search space will be used.</param>
+        /// <param name="sdcaLogisticRegressionSearchSpace">if provided, use it as search space for <see cref="SdcaLogisticRegressionBinaryTrainer"/>, otherwise the default search space will be used.</param>
         /// <returns></returns>
-        public SweepablePipeline BinaryClassification(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
-            FastTreeOption fastTreeOption = null, LgbmOption lgbmOption = null, FastForestOption fastForestOption = null, LbfgsOption lbfgsOption = null, SdcaOption sdcaOption = null,
-            SearchSpace<FastTreeOption> fastTreeSearchSpace = null, SearchSpace<LgbmOption> lgbmSearchSpace = null, SearchSpace<FastForestOption> fastForestSearchSpace = null, SearchSpace<LbfgsOption> lbfgsSearchSpace = null, SearchSpace<SdcaOption> sdcaSearchSpace = null)
+        public SweepablePipeline BinaryClassification(string labelColumnName = DefaultColumnNames.Label,
+            string featureColumnName = DefaultColumnNames.Features,
+            string exampleWeightColumnName = null,
+            bool useFastForest = true,
+            bool useLgbm = true,
+            bool useFastTree = true,
+            bool useLbfgsLogisticRegression = true,
+            bool useSdcaLogisticRegression = true,
+            FastTreeOption fastTreeOption = null,
+            LgbmOption lgbmOption = null,
+            FastForestOption fastForestOption = null,
+            LbfgsOption lbfgsLogisticRegressionOption = null,
+            SdcaOption sdcaLogisticRegressionOption = null,
+            SearchSpace<FastTreeOption> fastTreeSearchSpace = null,
+            SearchSpace<LgbmOption> lgbmSearchSpace = null,
+            SearchSpace<FastForestOption> fastForestSearchSpace = null,
+            SearchSpace<LbfgsOption> lbfgsLogisticRegressionSearchSpace = null,
+            SearchSpace<SdcaOption> sdcaLogisticRegressionSearchSpace = null)
         {
             var res = new List<SweepableEstimator>();
 
@@ -359,22 +375,22 @@ public SweepablePipeline BinaryClassification(string labelColumnName = DefaultCo
                 res.Add(SweepableEstimatorFactory.CreateLightGbmBinary(lgbmOption, lgbmSearchSpace ?? new SearchSpace<LgbmOption>(lgbmOption)));
             }
 
-            if (useLbfgs)
+            if (useLbfgsLogisticRegression)
             {
-                lbfgsOption = lbfgsOption ?? new LbfgsOption();
-                lbfgsOption.LabelColumnName = labelColumnName;
-                lbfgsOption.FeatureColumnName = featureColumnName;
-                lbfgsOption.ExampleWeightColumnName = exampleWeightColumnName;
-                res.Add(SweepableEstimatorFactory.CreateLbfgsLogisticRegressionBinary(lbfgsOption, lbfgsSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsOption)));
+                lbfgsLogisticRegressionOption = lbfgsLogisticRegressionOption ?? new LbfgsOption();
+                lbfgsLogisticRegressionOption.LabelColumnName = labelColumnName;
+                lbfgsLogisticRegressionOption.FeatureColumnName = featureColumnName;
+                lbfgsLogisticRegressionOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateLbfgsLogisticRegressionBinary(lbfgsLogisticRegressionOption, lbfgsLogisticRegressionSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsLogisticRegressionOption)));
             }
 
-            if (useSdca)
+            if (useSdcaLogisticRegression)
             {
-                sdcaOption = sdcaOption ?? new SdcaOption();
-                sdcaOption.LabelColumnName = labelColumnName;
-                sdcaOption.FeatureColumnName = featureColumnName;
-                sdcaOption.ExampleWeightColumnName = exampleWeightColumnName;
-                res.Add(SweepableEstimatorFactory.CreateSdcaLogisticRegressionBinary(sdcaOption, sdcaSearchSpace ?? new SearchSpace<SdcaOption>(sdcaOption)));
+                sdcaLogisticRegressionOption = sdcaLogisticRegressionOption ?? new SdcaOption();
+                sdcaLogisticRegressionOption.LabelColumnName = labelColumnName;
+                sdcaLogisticRegressionOption.FeatureColumnName = featureColumnName;
+                sdcaLogisticRegressionOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateSdcaLogisticRegressionBinary(sdcaLogisticRegressionOption, sdcaLogisticRegressionSearchSpace ?? new SearchSpace<SdcaOption>(sdcaLogisticRegressionOption)));
             }
 
             return new SweepablePipeline().Append(res.ToArray());
@@ -389,22 +405,50 @@ public SweepablePipeline BinaryClassification(string labelColumnName = DefaultCo
         /// <param name="useFastForest">true if use fast forest as available trainer.</param>
         /// <param name="useLgbm">true if use lgbm as available trainer.</param>
         /// <param name="useFastTree">true if use fast tree as available trainer.</param>
-        /// <param name="useLbfgs">true if use lbfgs as available trainer.</param>
-        /// <param name="useSdca">true if use sdca as available trainer.</param>
+        /// <param name="useLbfgsMaximumEntrophy">true if use <see cref="LbfgsMaximumEntropyMulticlassTrainer"/> as available trainer.</param>
+        /// <param name="useLbfgsLogisticRegression">true if use <see cref="LbfgsLogisticRegressionBinaryTrainer"/> as available trainer.</param>
+        /// <param name="useSdcaMaximumEntrophy">true if use <see cref="SdcaMaximumEntropyMulticlassTrainer"/> as available trainer.</param>
+        /// <param name="useSdcaLogisticRegression">true if use <see cref="SdcaLogisticRegressionBinaryTrainer"/> as available trainer.</param>
         /// <param name="fastTreeOption">if provided, use it as initial option for fast tree, otherwise the default option will be used.</param>
         /// <param name="lgbmOption">if provided, use it as initial option for lgbm, otherwise the default option will be used.</param>
         /// <param name="fastForestOption">if provided, use it as initial option for fast forest, otherwise the default option will be used.</param>
-        /// <param name="lbfgsOption">if provided, use it as initial option for lbfgs, otherwise the default option will be used.</param>
-        /// <param name="sdcaOption">if provided, use it as initial option for sdca, otherwise the default option will be used.</param>
+        /// <param name="lbfgsMaximumEntrophyOption">if provided, use it as initial option for <paramref name="lbfgsMaximumEntrophySearchSpace"/>, otherwise the default option will be used.</param>
+        /// <param name="lbfgsLogisticRegressionOption">if provided, use it as initial option for <paramref name="lbfgsLogisticRegressionSearchSpace"/>, otherwise the default option will be used.</param>
+        /// <param name="sdcaMaximumEntrophyOption">if provided, use it as initial option for <paramref name="sdcaMaximumEntorphySearchSpace"/>, otherwise the default option will be used.</param>
+        /// <param name="sdcaLogisticRegressionOption">if provided, use it as initial option for <paramref name="sdcaLogisticRegressionSearchSpace"/>, otherwise the default option will be used.</param>
         /// <param name="fastTreeSearchSpace">if provided, use it as search space for fast tree, otherwise the default search space will be used.</param>
         /// <param name="lgbmSearchSpace">if provided, use it as search space for lgbm, otherwise the default search space will be used.</param>
         /// <param name="fastForestSearchSpace">if provided, use it as search space for fast forest, otherwise the default search space will be used.</param>
-        /// <param name="lbfgsSearchSpace">if provided, use it as search space for lbfgs, otherwise the default search space will be used.</param>
-        /// <param name="sdcaSearchSpace">if provided, use it as search space for sdca, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsMaximumEntrophySearchSpace">if provided, use it as search space for <see cref="LbfgsMaximumEntropyMulticlassTrainer"/>, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsLogisticRegressionSearchSpace">if provided, use it as search space for <see cref="LbfgsMaximumEntropyMulticlassTrainer"/>, otherwise the default search space will be used.</param>
+        /// <param name="sdcaMaximumEntorphySearchSpace">if provided, use it as search space for <see cref="SdcaMaximumEntropyMulti"/>, otherwise the default search space will be used.</param>
+        /// <param name="sdcaLogisticRegressionSearchSpace">if provided, use it as search space for <see cref="SdcaLogisticRegressionBinaryTrainer"/>, otherwise the default search space will be used.</param>
         /// <returns></returns>
-        public SweepablePipeline MultiClassification(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
-            FastTreeOption fastTreeOption = null, LgbmOption lgbmOption = null, FastForestOption fastForestOption = null, LbfgsOption lbfgsOption = null, SdcaOption sdcaOption = null,
-            SearchSpace<FastTreeOption> fastTreeSearchSpace = null, SearchSpace<LgbmOption> lgbmSearchSpace = null, SearchSpace<FastForestOption> fastForestSearchSpace = null, SearchSpace<LbfgsOption> lbfgsSearchSpace = null, SearchSpace<SdcaOption> sdcaSearchSpace = null)
+        public SweepablePipeline MultiClassification(
+            string labelColumnName = DefaultColumnNames.Label,
+            string featureColumnName = DefaultColumnNames.Features,
+            string exampleWeightColumnName = null,
+            bool useFastForest = true,
+            bool useLgbm = true,
+            bool useFastTree = true,
+            bool useLbfgsMaximumEntrophy = true,
+            bool useLbfgsLogisticRegression = true,
+            bool useSdcaMaximumEntrophy = true,
+            bool useSdcaLogisticRegression = true,
+            FastTreeOption fastTreeOption = null,
+            LgbmOption lgbmOption = null,
+            FastForestOption fastForestOption = null,
+            LbfgsOption lbfgsMaximumEntrophyOption = null,
+            LbfgsOption lbfgsLogisticRegressionOption = null,
+            SdcaOption sdcaMaximumEntrophyOption = null,
+            SdcaOption sdcaLogisticRegressionOption = null,
+            SearchSpace<FastTreeOption> fastTreeSearchSpace = null,
+            SearchSpace<LgbmOption> lgbmSearchSpace = null,
+            SearchSpace<FastForestOption> fastForestSearchSpace = null,
+            SearchSpace<LbfgsOption> lbfgsMaximumEntrophySearchSpace = null,
+            SearchSpace<LbfgsOption> lbfgsLogisticRegressionSearchSpace = null,
+            SearchSpace<SdcaOption> sdcaMaximumEntorphySearchSpace = null,
+            SearchSpace<SdcaOption> sdcaLogisticRegressionSearchSpace = null)
         {
             var res = new List<SweepableEstimator>();
 
@@ -435,24 +479,41 @@ public SweepablePipeline MultiClassification(string labelColumnName = DefaultCol
                 res.Add(SweepableEstimatorFactory.CreateLightGbmMulti(lgbmOption, lgbmSearchSpace ?? new SearchSpace<LgbmOption>(lgbmOption)));
             }
 
-            if (useLbfgs)
+            if (useLbfgsLogisticRegression)
             {
-                lbfgsOption = lbfgsOption ?? new LbfgsOption();
-                lbfgsOption.LabelColumnName = labelColumnName;
-                lbfgsOption.FeatureColumnName = featureColumnName;
-                lbfgsOption.ExampleWeightColumnName = exampleWeightColumnName;
-                res.Add(SweepableEstimatorFactory.CreateLbfgsLogisticRegressionOva(lbfgsOption, lbfgsSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsOption)));
-                res.Add(SweepableEstimatorFactory.CreateLbfgsMaximumEntropyMulti(lbfgsOption, lbfgsSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsOption)));
+                lbfgsLogisticRegressionOption = lbfgsLogisticRegressionOption ?? new LbfgsOption();
+                lbfgsLogisticRegressionOption.LabelColumnName = labelColumnName;
+                lbfgsLogisticRegressionOption.FeatureColumnName = featureColumnName;
+                lbfgsLogisticRegressionOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateLbfgsLogisticRegressionOva(lbfgsLogisticRegressionOption, lbfgsLogisticRegressionSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsLogisticRegressionOption)));
             }
 
-            if (useSdca)
+
+            if (useLbfgsMaximumEntrophy)
             {
-                sdcaOption = sdcaOption ?? new SdcaOption();
-                sdcaOption.LabelColumnName = labelColumnName;
-                sdcaOption.FeatureColumnName = featureColumnName;
-                sdcaOption.ExampleWeightColumnName = exampleWeightColumnName;
-                res.Add(SweepableEstimatorFactory.CreateSdcaMaximumEntropyMulti(sdcaOption, sdcaSearchSpace ?? new SearchSpace<SdcaOption>(sdcaOption)));
-                res.Add(SweepableEstimatorFactory.CreateSdcaLogisticRegressionOva(sdcaOption, sdcaSearchSpace ?? new SearchSpace<SdcaOption>(sdcaOption)));
+                lbfgsMaximumEntrophyOption = lbfgsMaximumEntrophyOption ?? new LbfgsOption();
+                lbfgsMaximumEntrophyOption.LabelColumnName = labelColumnName;
+                lbfgsMaximumEntrophyOption.FeatureColumnName = featureColumnName;
+                lbfgsMaximumEntrophyOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateLbfgsMaximumEntropyMulti(lbfgsMaximumEntrophyOption, lbfgsMaximumEntrophySearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsMaximumEntrophyOption)));
+            }
+
+            if (useSdcaMaximumEntrophy)
+            {
+                sdcaMaximumEntrophyOption = sdcaMaximumEntrophyOption ?? new SdcaOption();
+                sdcaMaximumEntrophyOption.LabelColumnName = labelColumnName;
+                sdcaMaximumEntrophyOption.FeatureColumnName = featureColumnName;
+                sdcaMaximumEntrophyOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateSdcaMaximumEntropyMulti(sdcaMaximumEntrophyOption, sdcaMaximumEntorphySearchSpace ?? new SearchSpace<SdcaOption>(sdcaMaximumEntrophyOption)));
+            }
+
+            if (useSdcaLogisticRegression)
+            {
+                sdcaLogisticRegressionOption = sdcaLogisticRegressionOption ?? new SdcaOption();
+                sdcaLogisticRegressionOption.LabelColumnName = labelColumnName;
+                sdcaLogisticRegressionOption.FeatureColumnName = featureColumnName;
+                sdcaLogisticRegressionOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateSdcaLogisticRegressionOva(sdcaLogisticRegressionOption, sdcaLogisticRegressionSearchSpace ?? new SearchSpace<SdcaOption>(sdcaLogisticRegressionOption)));
             }
 
             return new SweepablePipeline().Append(res.ToArray());
@@ -467,22 +528,38 @@ public SweepablePipeline MultiClassification(string labelColumnName = DefaultCol
         /// <param name="useFastForest">true if use fast forest as available trainer.</param>
         /// <param name="useLgbm">true if use lgbm as available trainer.</param>
         /// <param name="useFastTree">true if use fast tree as available trainer.</param>
-        /// <param name="useLbfgs">true if use lbfgs as available trainer.</param>
-        /// <param name="useSdca">true if use sdca as available trainer.</param>
+        /// <param name="useLbfgsPoissonRegression">true if use <see cref="LbfgsPoissonRegressionTrainer"/> as available trainer.</param>
+        /// <param name="useSdca">true if use <see cref="SdcaRegressionTrainer"/> as available trainer.</param>
         /// <param name="fastTreeOption">if provided, use it as initial option for fast tree, otherwise the default option will be used.</param>
         /// <param name="lgbmOption">if provided, use it as initial option for lgbm, otherwise the default option will be used.</param>
         /// <param name="fastForestOption">if provided, use it as initial option for fast forest, otherwise the default option will be used.</param>
-        /// <param name="lbfgsOption">if provided, use it as initial option for lbfgs, otherwise the default option will be used.</param>
-        /// <param name="sdcaOption">if provided, use it as initial option for sdca, otherwise the default option will be used.</param>
+        /// <param name="lbfgsPoissonRegressionOption">if provided, use it as initial option for <paramref name="lbfgsPoissonRegressionSearchSpace"/>, otherwise the default option will be used.</param>
+        /// <param name="sdcaOption">if provided, use it as initial option for <paramref name="sdcaSearchSpace"/>, otherwise the default option will be used.</param>
         /// <param name="fastTreeSearchSpace">if provided, use it as search space for fast tree, otherwise the default search space will be used.</param>
         /// <param name="lgbmSearchSpace">if provided, use it as search space for lgbm, otherwise the default search space will be used.</param>
         /// <param name="fastForestSearchSpace">if provided, use it as search space for fast forest, otherwise the default search space will be used.</param>
-        /// <param name="lbfgsSearchSpace">if provided, use it as search space for lbfgs, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsPoissonRegressionSearchSpace">if provided, use it as search space for <see cref="LbfgsPoissonRegressionTrainer"/>, otherwise the default search space will be used.</param>
         /// <param name="sdcaSearchSpace">if provided, use it as search space for sdca, otherwise the default search space will be used.</param>
         /// <returns></returns>
-        public SweepablePipeline Regression(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
-            FastTreeOption fastTreeOption = null, LgbmOption lgbmOption = null, FastForestOption fastForestOption = null, LbfgsOption lbfgsOption = null, SdcaOption sdcaOption = null,
-            SearchSpace<FastTreeOption> fastTreeSearchSpace = null, SearchSpace<LgbmOption> lgbmSearchSpace = null, SearchSpace<FastForestOption> fastForestSearchSpace = null, SearchSpace<LbfgsOption> lbfgsSearchSpace = null, SearchSpace<SdcaOption> sdcaSearchSpace = null)
+        public SweepablePipeline Regression(
+            string labelColumnName = DefaultColumnNames.Label,
+            string featureColumnName = DefaultColumnNames.Features,
+            string exampleWeightColumnName = null,
+            bool useFastForest = true,
+            bool useLgbm = true,
+            bool useFastTree = true,
+            bool useLbfgsPoissonRegression = true,
+            bool useSdca = true,
+            FastTreeOption fastTreeOption = null,
+            LgbmOption lgbmOption = null,
+            FastForestOption fastForestOption = null,
+            LbfgsOption lbfgsPoissonRegressionOption = null,
+            SdcaOption sdcaOption = null,
+            SearchSpace<FastTreeOption> fastTreeSearchSpace = null,
+            SearchSpace<LgbmOption> lgbmSearchSpace = null,
+            SearchSpace<FastForestOption> fastForestSearchSpace = null,
+            SearchSpace<LbfgsOption> lbfgsPoissonRegressionSearchSpace = null,
+            SearchSpace<SdcaOption> sdcaSearchSpace = null)
         {
             var res = new List<SweepableEstimator>();
 
@@ -513,13 +590,13 @@ public SweepablePipeline Regression(string labelColumnName = DefaultColumnNames.
                 res.Add(SweepableEstimatorFactory.CreateLightGbmRegression(lgbmOption, lgbmSearchSpace ?? new SearchSpace<LgbmOption>(lgbmOption)));
             }
 
-            if (useLbfgs)
+            if (useLbfgsPoissonRegression)
             {
-                lbfgsOption = lbfgsOption ?? new LbfgsOption();
-                lbfgsOption.LabelColumnName = labelColumnName;
-                lbfgsOption.FeatureColumnName = featureColumnName;
-                lbfgsOption.ExampleWeightColumnName = exampleWeightColumnName;
-                res.Add(SweepableEstimatorFactory.CreateLbfgsPoissonRegressionRegression(lbfgsOption, lbfgsSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsOption)));
+                lbfgsPoissonRegressionOption = lbfgsPoissonRegressionOption ?? new LbfgsOption();
+                lbfgsPoissonRegressionOption.LabelColumnName = labelColumnName;
+                lbfgsPoissonRegressionOption.FeatureColumnName = featureColumnName;
+                lbfgsPoissonRegressionOption.ExampleWeightColumnName = exampleWeightColumnName;
+                res.Add(SweepableEstimatorFactory.CreateLbfgsPoissonRegressionRegression(lbfgsPoissonRegressionOption, lbfgsPoissonRegressionSearchSpace ?? new SearchSpace<LbfgsOption>(lbfgsPoissonRegressionOption)));
             }
 
             if (useSdca)
diff --git a/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs b/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
index f658a6f1aa..761aac2f4c 100644
--- a/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
+++ b/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
@@ -327,12 +327,12 @@ private SweepablePipeline CreateBinaryClassificationPipeline(IDataView trainData
             if (preFeaturizer != null)
             {
                 return preFeaturizer.Append(Context.Auto().Featurizer(trainData, columnInformation, Features))
-                                        .Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgs: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
+                                        .Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
             }
             else
             {
                 return Context.Auto().Featurizer(trainData, columnInformation, Features)
-                           .Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgs: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
+                           .Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
             }
         }
     }
diff --git a/src/Microsoft.ML.AutoML/API/MulticlassClassificationExperiment.cs b/src/Microsoft.ML.AutoML/API/MulticlassClassificationExperiment.cs
index 836eddedb5..b7344e7b0f 100644
--- a/src/Microsoft.ML.AutoML/API/MulticlassClassificationExperiment.cs
+++ b/src/Microsoft.ML.AutoML/API/MulticlassClassificationExperiment.cs
@@ -313,8 +313,9 @@ private protected override RunDetail<MulticlassClassificationMetrics> GetBestRun
 
         private SweepablePipeline CreateMulticlassClassificationPipeline(IDataView trainData, ColumnInformation columnInformation, IEstimator<ITransformer> preFeaturizer = null)
         {
-            var useSdca = Settings.Trainers.Contains(MulticlassClassificationTrainer.SdcaMaximumEntropy);
-            var uselbfgs = Settings.Trainers.Contains(MulticlassClassificationTrainer.LbfgsLogisticRegressionOva);
+            var useSdcaMaximumEntrophy = Settings.Trainers.Contains(MulticlassClassificationTrainer.SdcaMaximumEntropy);
+            var uselbfgsLR = Settings.Trainers.Contains(MulticlassClassificationTrainer.LbfgsLogisticRegressionOva);
+            var uselbfgsME = Settings.Trainers.Contains(MulticlassClassificationTrainer.LbfgsMaximumEntropy);
             var useLgbm = Settings.Trainers.Contains(MulticlassClassificationTrainer.LightGbm);
             var useFastForest = Settings.Trainers.Contains(MulticlassClassificationTrainer.FastForestOva);
             var useFastTree = Settings.Trainers.Contains(MulticlassClassificationTrainer.FastTreeOva);
@@ -329,7 +330,7 @@ private SweepablePipeline CreateMulticlassClassificationPipeline(IDataView train
 
             pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features));
             pipeline = pipeline.Append(Context.Transforms.Conversion.MapValueToKey(label, label));
-            pipeline = pipeline.Append(Context.Auto().MultiClassification(label, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgs: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
+            pipeline = pipeline.Append(Context.Auto().MultiClassification(label, useSdcaMaximumEntrophy: useSdcaMaximumEntrophy, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsMaximumEntrophy: uselbfgsME, useLbfgsLogisticRegression: uselbfgsLR, useFastForest: useFastForest, featureColumnName: Features));
             pipeline = pipeline.Append(Context.Transforms.Conversion.MapKeyToValue(DefaultColumnNames.PredictedLabel, DefaultColumnNames.PredictedLabel));
 
             return pipeline;
diff --git a/src/Microsoft.ML.AutoML/API/RegressionExperiment.cs b/src/Microsoft.ML.AutoML/API/RegressionExperiment.cs
index a836b6c0c0..3d93a63b0a 100644
--- a/src/Microsoft.ML.AutoML/API/RegressionExperiment.cs
+++ b/src/Microsoft.ML.AutoML/API/RegressionExperiment.cs
@@ -310,7 +310,7 @@ private SweepablePipeline CreateRegressionPipeline(IDataView trainData, ColumnIn
 
             var label = columnInformation.LabelColumnName;
             pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features));
-            pipeline = pipeline.Append(Context.Auto().Regression(label, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgs: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
+            pipeline = pipeline.Append(Context.Auto().Regression(label, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsPoissonRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
 
             return pipeline;
         }
diff --git a/test/Microsoft.ML.AutoML.Tests/AutoMLExperimentTests.cs b/test/Microsoft.ML.AutoML.Tests/AutoMLExperimentTests.cs
index 6d14b266bb..18a0245718 100644
--- a/test/Microsoft.ML.AutoML.Tests/AutoMLExperimentTests.cs
+++ b/test/Microsoft.ML.AutoML.Tests/AutoMLExperimentTests.cs
@@ -212,7 +212,7 @@ public async Task AutoMLExperiment_UCI_Adult_Train_Test_Split_Test()
             var data = DatasetUtil.GetUciAdultDataView();
             var experiment = context.Auto().CreateExperiment();
             var pipeline = context.Auto().Featurizer(data, "_Features_", excludeColumns: new[] { DatasetUtil.UciAdultLabel })
-                                .Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", useLgbm: false, useSdca: false, useLbfgs: false));
+                                .Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", useLgbm: false, useSdcaLogisticRegression: false, useLbfgsLogisticRegression: false));
 
             experiment.SetDataset(context.Data.TrainTestSplit(data))
                     .SetBinaryClassificationMetric(BinaryClassificationMetric.AreaUnderRocCurve, DatasetUtil.UciAdultLabel)
@@ -237,7 +237,7 @@ public async Task AutoMLExperiment_UCI_Adult_CV_5_Test()
             var data = DatasetUtil.GetUciAdultDataView();
             var experiment = context.Auto().CreateExperiment();
             var pipeline = context.Auto().Featurizer(data, "_Features_", excludeColumns: new[] { DatasetUtil.UciAdultLabel })
-                                .Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", useLgbm: false, useSdca: false, useLbfgs: false));
+                                .Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", useLgbm: false, useSdcaLogisticRegression: false, useLbfgsLogisticRegression: false));
 
             experiment.SetDataset(data, 5)
                     .SetBinaryClassificationMetric(BinaryClassificationMetric.AreaUnderRocCurve, DatasetUtil.UciAdultLabel)
@@ -264,7 +264,7 @@ public async Task AutoMLExperiment_Iris_CV_5_Test()
             var label = "Label";
             var pipeline = context.Auto().Featurizer(data, excludeColumns: new[] { label })
                                 .Append(context.Transforms.Conversion.MapValueToKey(label, label))
-                                .Append(context.Auto().MultiClassification(label, useLgbm: false, useSdca: false, useLbfgs: false));
+                                .Append(context.Auto().MultiClassification(label, useLgbm: false, useSdcaMaximumEntrophy: false, useLbfgsMaximumEntrophy: false));
 
             experiment.SetDataset(data, 5)
                     .SetMulticlassClassificationMetric(MulticlassClassificationMetric.MacroAccuracy, label)
@@ -291,7 +291,7 @@ public async Task AutoMLExperiment_Iris_Train_Test_Split_Test()
             var label = "Label";
             var pipeline = context.Auto().Featurizer(data, excludeColumns: new[] { label })
                                 .Append(context.Transforms.Conversion.MapValueToKey(label, label))
-                                .Append(context.Auto().MultiClassification(label, useLgbm: false, useSdca: false, useLbfgs: false));
+                                .Append(context.Auto().MultiClassification(label, useLgbm: false, useSdcaMaximumEntrophy: false, useLbfgsMaximumEntrophy: false));
 
             experiment.SetDataset(context.Data.TrainTestSplit(data))
                     .SetMulticlassClassificationMetric(MulticlassClassificationMetric.MacroAccuracy, label)
@@ -318,7 +318,7 @@ public async Task AutoMLExperiment_Taxi_Fare_Train_Test_Split_Test()
             var experiment = context.Auto().CreateExperiment();
             var label = DatasetUtil.TaxiFareLabel;
             var pipeline = context.Auto().Featurizer(train, excludeColumns: new[] { label })
-                                .Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgs: false));
+                                .Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgsPoissonRegression: false));
 
             experiment.SetDataset(train, test)
                     .SetRegressionMetric(RegressionMetric.RSquared, label)
@@ -337,7 +337,7 @@ public async Task AutoMLExperiment_Taxi_Fare_CV_5_Test()
             var experiment = context.Auto().CreateExperiment();
             var label = DatasetUtil.TaxiFareLabel;
             var pipeline = context.Auto().Featurizer(train, excludeColumns: new[] { label })
-                                .Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgs: false));
+                                .Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgsPoissonRegression: false));
 
             experiment.SetDataset(train, 5)
                     .SetRegressionMetric(RegressionMetric.RSquared, label)