Abstract: A method of generating an uncertainty score for an output of a Gradient Boosted Decision Tree (GBDT) model is disclosed. The output is a prediction of the GBDT model for an in-use dataset. The method comprises acquiring the GBDT model including a sequence of trees beginning with an initial tree and ending with a last tree, a given one of the sequence of trees having been stochastically built during a current training iteration of the GBDT model, and defining a plurality of sub-sequences of trees in the sequence of trees as sub-models of the GBDT model. During a given in-use iteration of the GBDT model executable for the in-use dataset, the method comprises generating a plurality of sub-outputs using the respective sub-models and generating the uncertainty score using the plurality of sub-outputs, the uncertainty score being indicative of how different sub-outputs from the plurality of sub-outputs are amongst each other.

Abstract: A method and system for ranking digital object based on an objective characteristic associated therewith are provided. The method comprising: generating a set of digital objects based on a user request, the set of digital objects being rankable according to an objective characteristic thereof; receiving a filter request from the user, the filter request being based on a secondary characteristic of digital objects in the set of digital objects; determining object parameters for the digital objects in the set of digital objects, a given object parameter being indicative of a likelihood that an inclusion of a respective digital object in a re-ranked set of digital objects will increase a quality metric of the re-ranked set of digital objects; selecting digital objects based on object parameters; ranking digital objects based on respective values of the secondary characteristic, thereby generating the re-ranked set of digital objects.

Abstract: Methods and servers for of training a decision-tree based Machine Learning Algorithm (MLA) are disclosed. During a given training iteration, the method includes generating prediction values using current generated trees, generating estimated gradient values by applying a loss function, generating a first plurality of noisy estimated gradient values based on the estimated gradient values, generating a plurality of noisy candidate trees using the first plurality of noisy estimated gradient values, applying a selection metric to select a target tree amongst the plurality of noisy candidate trees, generating a second plurality of noisy estimated gradient values based on the plurality of estimated gradient values, generating an iteration-specific tree based on the target tree and the second plurality of noisy estimated gradient values, and storing, the iteration-specific tree to be used in combination with the current generated trees.

Abstract: Method and server for training a Machine Learning Algorithm (MLA) for ranking objects in response to a query are disclosed. The training includes use of a ranking quality metric function that is one of a flat and a discontinuous function to determine a performance score of the MLA. The method includes generating relevance scores for a set of training objects based on data associated with the set of training objects and a training query, generating noise-induced relevance scores for the set of training objects by combining the relevance scores and noise values, generating the performance score for the MLA based on the noise-induced relevance scores, determining a policy gradient value for adjusting relevance scores to be generated by the MLA for the in-use objects in response to the in-use query, and applying the policy gradient value for training the MLA to rank in-use objects in response to an in-use query.

Abstract: A method and system for ranking digital object based on an objective characteristic associated therewith are provided. The method comprising: generating a set of digital objects based on a user request, the set of digital objects being rankable according to an objective characteristic thereof; receiving a filter request from the user, the filter request being based on a secondary characteristic of digital objects in the set of digital objects; determining object parameters for the digital objects in the set of digital objects, a given object parameter being indicative of a likelihood that an inclusion of a respective digital object in a re-ranked set of digital objects will increase a quality metric of the re-ranked set of digital objects; selecting digital objects based on object parameters; ranking digital objects based on respective values of the secondary characteristic, thereby generating the re-ranked set of digital objects.