Abstract: Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

Abstract: Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

Abstract: Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing an electrical grid by controlling devices connected to the electrical grid. In one aspect, a method comprises: autonomously determining whether a control strategy update criterion is satisfied, and in response to determining that the control strategy update criterion is satisfied: identifying: (i) a plurality of devices connected to the electrical grid, and (ii) one or more objectives to be satisfied by the plurality of devices; generating, for the plurality of devices and for a plurality of controls, simulated data that simulates operation of the plurality of devices based on application of the plurality of controls; and identifying, by an optimization procedure and based on at least the simulated data, a respective control to be applied to each of the plurality of devices to satisfy the objectives.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing an electrical grid by controlling devices connected to the electrical grid. In one aspect, a method comprises: autonomously determining whether a control strategy update criterion is satisfied, and in response to determining that the control strategy update criterion is satisfied: identifying: (i) a plurality of devices connected to the electrical grid, and (ii) one or more objectives to be satisfied by the plurality of devices; generating, for the plurality of devices and for a plurality of controls, simulated data that simulates operation of the plurality of devices based on application of the plurality of controls; and identifying, by an optimization procedure and based on at least the simulated data, a respective control to be applied to each of the plurality of devices to satisfy the objectives.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a load forecast by predicting load measurements at future time points, wherein a load measurement at a time point characterizes a cumulative measure of energy consumption by a population of energy consuming devices at the time point.