Abstract: Various methods and processes, apparatuses/systems, and media for watermarking generative tabular data in a flexible and robust manner are disclosed. A processor partitions a feature space into a pair of columns (key, value) by calling a subroutine; divides a range of features in each key column into bins of a predefined size (1/b) to form b consecutive intervals; computes a hash by utilizing a center of the bins for each key column which becomes a seed for a random number generator; randomly generates, by utilizing the random number generator, predefined first and second color-coded intervals for corresponding value column, wherein each color-coded interval is of size 1/b; and embeds a watermark in the continuous features of the value column by applying an algorithm such that a feature of the value column in the first color-coded interval moves to nearest second color-coded interval.

Abstract: A method for facilitating supervised generative optimization for synthetic data generation is disclosed. The method includes receiving, via an application programming interface, inputs that include input data and parameters; partitioning the input data to generate data sets, the data sets including training data sets, validation data sets, and test data sets; tuning hyperparameters of synthesizers by using the data sets and supervised optimization that is based on downstream performance metrics; determining a mixture distribution from among the tuned synthesizers; training machine learning models based on the mixture distribution; and generating, by using the trained machine learning models, sets of synthetic data based on the input data.

Abstract: A method for providing a data-driven tool for individualized financial planning is provided. The method includes: receiving first information that relates to a user, the first information including at least one financial goal of the user; applying, to the first information, a machine learning algorithm that is uses historical data that relates to financial outcomes; calculating, based on an output of the machine learning algorithm, a probability that the at least one financial goal of the user is achievable; and determining, based on an output the machine learning algorithm, a proposed sequence of actions to be taken by the user with respect to achieving the at least one financial goal. The method may further include applying an artificial intelligence (AI) algorithm that uses a Monte Carlo tree search (MCTS) technique with respect to potential user actions.