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- SPARK-39114
ml.optim.aggregator avoid re-allocating buffers - SPARK-35707
optimize sparse GEMM by skipping bound checking - SPARK-35678
add a common softmax function - SPARK-35666
add new gemv to skip array shape checking - SPARK-35619
Refactor LinearRegression - make huber support virtual centering - SPARK-35100
Refactor AFT - support virtual centering - SPARK-35024
Refactor LinearSVC - support virtual centering - SPARK-34860
Multinomial Logistic Regression with intercept support centering - SPARK-34858
Binary Logistic Regression with intercept support centering - SPARK-34797
Refactor Logistic Aggregator - support virtual centering - SPARK-34765
Linear Models standardization optimization - SPARK-32907
adaptively blockify instances - SPARK-32061
potential regression if use memoryUsage instead of numRows - SPARK-32060
Huber loss Convergence - SPARK-31976
use MemoryUsage to control the size of block - SPARK-31783
Performance test on dense and sparse datasets - SPARK-31714
Performance test on java vectorization vs dot vs gemv vs gemm - SPARK-31661
Document usage of blockSize - SPARK-31656
AFT blockify input vectors - SPARK-30699
GMM blockify input vectors - SPARK-30662
ALS/MLP extend HasBlockSize - SPARK-30661
KMeans blockify input vectors - SPARK-30660
LinearRegression blockify input vectors - SPARK-30659
LogisticRegression blockify input vectors - SPARK-30642
LinearSVC blockify input vectors
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