Abstract: Systems, methods, and processor-readable media for elastic allocation of resources for deep learning jobs are described. A machine-learning-as-a-service (MLaaS) of a cloud computing system includes an elastic training module which includes resource allocator for allocating resources to training jobs that optimizes overall estimated time to completion (ETC) for all training jobs received by the system and uses node-based resource allocation. The elastic training module may realize a combination of high resource utilization, short training times, and low queueing delay relative to existing approaches, thereby potentially enabling the realization of higher profits for a cloud computing system which provides MLaaS to users (i.e. customers). An improved user interface is described, enabling users to specify a range of resources to elastically allocate to the user's training job, and/or informing users of training time saved through the use of elastic resource allocation.

Abstract: Systems and methods for formulating a prediction model. A linear prediction of an expert model is received, wherein given point xi, the linear prediction is gi:=g(xi)=gTxi+g0, the expert model having an expert model feature list. New data (xi,yi)?i?[1,N] is received, wherein the expert model feature list is a subset of a new feature list of the new data. The prediction model is formulated as min w ? i = 1 N ( f i ( w ) - y i ) 2 + ? ? ( f i ( w ) - g i ) 2 , wherein fx(w)?c1, ?x?X, and fx(w)?c3, ?x?X?H. ? is a positive number assigning weight to the linear prediction.

Abstract: Methods, systems, and computer-readable media for using artificial intelligence to assist a linear programming (LP) solver are disclosed. A LP assistance software system leverages the categorization of variables to improve LP solver efficiency at the pricing step and/or to generate a custom initial basis for the first iteration of the simplex method. The LP assistance software system may thereby improve the standard simplex algorithm, which involves selecting individual variables in its pricing step.

Abstract: The present disclosure provides a computer implemented method and system for generating an algebraic modelling language (AML) formulation of natural language text description of an optimization problem. The computer implemented method includes generating, based on the natural language text description, a text markup language intermediate representation (IR) of the optimization problem, the text markup language IR including an IR objective declaration that defines an objective for the optimization problem and a first IR constraint declaration that indicates a first constraint for the optimization problem. The computer implemented also includes generating, based on the text markup language IR, the AML formulation of the optimization problem, the AML formulation including an AML objective declaration that defines the objective for the optimization problem and a first AML constraint declaration that indicates the first constraint for the optimization problem.

Abstract: The present disclosure provides a method and system for evaluating a machine learning model using an evaluation dataset for the machine learning model. The evaluation dataset includes for each entity in a group of entities: (i) an ordered set of attribute values for the entity, each attribute value corresponding to a respective attribute in a set of attributes that is common for all of the entities in the group of entities, and (ii) an outcome prediction generated for the entity by the machine learning model based on the ordered set of attribute values for the entity, wherein the outcome prediction generated for each entity is either a first outcome or a second outcome. Based on the evaluation dataset, using an optimization process, respective importance values are computed for the attributes, the respective importance values indicating attributes that are most responsible for the machine learning model predicting a first outcome.

Abstract: Servers, methods and systems are disclosed for fair and secure vertical federated learning. Fair and secure vertical federated learning (FSVFL) systems are disclosed that achieve one or more of the following properties: model fairness, high security, high accuracy, high efficiency, and/or high generality. Private data is retained on local computing systems, which share only their model outputs, and a server or a trusted computing system shares only model gradients and randomly partitioned sets of data sample identifiers with untrusted computing systems. A fairness constraint protects protected classes of data samples against model training resulting in bias on the basis of the protected classes.