Abstract: Systems, computer-implemented methods, and computer program products that can facilitate encoding a tree data structure into a vector based on a set of constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a constraint former that can form a set of constraints based on a first tree data structure and a vector encoder that can encode the first tree data structure into a vector based on the set of constraints.

Abstract: A random binning featurization process method, system, and computer program product for a distributed random binning featurization process on one or more multicore systems with a hybrid two-level parallelism, the method including in a training phase, receiving a first data matrix dividing the random binning featurization process into two orthogonal levels, in a high-level generating a randomized number of high-dimension grids and evenly partitioning the grids into nodes in a parallel system, and in a low-level, evenly partitioning dimensions in each grid to construct look-up tables of index vectors and compute a local feature matrix for each node.

Abstract: A block coordinate descent method, system, and computer program product for partitioning a global feature matrix into blocks, each node of the nodes of the blocks having a block size of a number of the blocks over a number of the nodes, selecting, at each node, a subset of the blocks from the blocks, and in one of the nodes, launching a thread to simultaneously update a closed-form solution by minimizing a single coordinate in one of the blocks.

Abstract: A method and system of creating a model for large scale data analytics is provided. Training data is received in a form of a data matrix X and partitioned into a plurality of partitions. A random matrix T is generated. A feature matrix is determined based on multiplying the partitioned training data by the random matrix T. A predicted data {tilde over (y)} is determined for each partition via a stochastic average gradient (SAG) of each partition. A number of SAG values is reduced based on a number of rows n in the data matrix X. For each iteration, a sum of the reduced SAG values is determined, as well as a full gradient based on the sum of the reduced SAG values from all rows n, by distributed parallel processing. The model parameters w are updated based on the full gradient for each partition.

Abstract: A block coordinate descent method, system, and computer program product for partitioning a global feature matrix into blocks, each node of the nodes of the blocks having a block size of a number of the blocks over a number of the nodes, selecting, at each node, a subset of the blocks from the blocks, and in one of the nodes, launching a thread to simultaneously update a closed-form solution by minimizing a single coordinate in one of the blocks.

Abstract: A random binning featurization process method, system, and computer program product for a distributed random binning featurization process on one or more multicore systems with a hybrid two-level parallelism, the method including in a training phase, receiving a first data matrix dividing the random binning featurization process into two orthogonal levels, in a high-level generating a randomized number of high-dimension grids and evenly partitioning the grids into nodes in a parallel system, and in a low-level, evenly partitioning dimensions in each grid to construct look-up tables of index vectors and compute a local feature matrix for each node.