8. A method for wireless communications, comprising: receiving information, at a server from a user equipment (UE), the information comprising a device identifier, a modem function executed by the UE, a selected hypothesis for the corresponding modem function, metrics for each feature associated with the corresponding modem function and state information, if the state information is available, in response to a trigger point associated with the modem function being met when executing the modem function; storing, by the server, the received information; running, by the server, one or more learning algorithms based on the stored information to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the state information was not provided by the UE; selecting, by the server, the unit sourced hypothesis and associated weights based on the one or more learning algorithms; running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a crowd sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; selecting, by the server, the crowd sourced hypothesis and associated weights based on the one or more learning algorithms; and sending, by the server to the UE, at least one of the selected unit sourced hypothesis and associated weights or the selected crowd sourced hypothesis and associated weights for the corresponding modem function.
8. A method for wireless communications, comprising: receiving information, at a server from a user equipment (UE), the information comprising a device identifier, a modem function executed by the UE, a selected hypothesis for the corresponding modem function, metrics for each feature associated with the corresponding modem function and state information, if the state information is available, in response to a trigger point associated with the modem function being met when executing the modem function; storing, by the server, the received information; running, by the server, one or more learning algorithms based on the stored information to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the state information was not provided by the UE; selecting, by the server, the unit sourced hypothesis and associated weights based on the one or more learning algorithms; running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a crowd sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; selecting, by the server, the crowd sourced hypothesis and associated weights based on the one or more learning algorithms; and sending, by the server to the UE, at least one of the selected unit sourced hypothesis and associated weights or the selected crowd sourced hypothesis and associated weights for the corresponding modem function. 11. The method of claim 8 , further comprising receiving by the server from the UE, a subset of hypotheses that the UE can use and the and the selected unit sourced hypothesis and the selected unit sourced hypothesis are selected from the subset of hypotheses stored on the UE, where the subset is less than a set of hypotheses stored on the UE.