Abstract: Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variables of each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing resource allocation. The method may comprise: receiving a resource request from a user; iterating through a plurality of entities to obtain a corresponding score for each entity based at least on: a projected approval rate for the entity to approve the resource request, a projected risk for the entity to serve the resource request, and one or more multipliers applied to the projected approval rate and the projected risk; and recommending one of the plurality of entities to serve the resource request for the user based on the corresponding score, wherein the one or more multipliers are obtained by solving an optimization model constructed based on historical data collected from a previous period of time, the historical data comprising approval rates projected for the plurality of entities during the previous period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping users are provided. One embodiment of the methods includes: dividing a plurality of users targeted by the an advertisement candidate into a plurality of user buckets, wherein each of the plurality of user buckets is associated with a first conversion score; obtaining a trained prediction model corresponding to the advertisement, wherein the trained prediction model is able to predict a conversion score based at least on the first conversion score associated with a user bucket and a second conversion score associated with a group of user buckets comprising the user bucket; and constructing an optimization model using the trained prediction model, wherein an objective function of the optimization problem is to maximize a total conversion score with a grouping strategy determined by solving the optimization problem.

Abstract: Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variables of each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

Abstract: Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variables of each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating risk are provided. One of the methods includes: obtaining a plurality of transactions each comprising a plurality of data dimensions, wherein some of the plurality of transactions are labeled as risky transactions and some of the plurality of transactions are labeled as safe transactions; obtaining at least one of the plurality of data dimensions as an output space and the plurality of data dimensions other than the at least one data dimension as an input space; initializing a first mapping from the input space to a latent space and a second mapping from the latent space to the output space, wherein the first mapping comprises mapping the input space to the latent space according to an inverse of a generalized linear model; and optimizing the first mapping and the second mapping to generate a Bezier surface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping users are provided. One embodiment of the methods includes: dividing a plurality of users targeted by the an advertisement candidate into a plurality of user buckets, wherein each of the plurality of user buckets is associated with a first conversion score; obtaining a trained prediction model corresponding to the advertisement, wherein the trained prediction model is able to predict a conversion score based at least on the first conversion score associated with a user bucket and a second conversion score associated with a group of user buckets comprising the user bucket; and constructing an optimization model using the trained prediction model, wherein an objective function of the optimization problem is to maximize a total conversion score with a grouping strategy determined by solving the optimization problem.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping a plurality of resources into a plurality of cohorts, wherein each of the plurality of resources is associated with a risk level and a benefit level, and resources in each cohort share the same risk level and the same benefit level; obtaining a plurality of provider-level adjustments; constructing an optimization model for determining a resource flow solution; and according to the plurality of cohort-provider-level adjustments, identifying one or more resources in each of the plurality of cohorts that are provided by a first provider to be transferred to a second provider; and automatically sending one or more requests to the first provider and the second provider to transfer the determined one or more first resources from the first provider to the second provider.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing resource allocation. The method may comprise: receiving a resource request from a user; iterating through a plurality of entities to obtain a corresponding score for each entity based at least on: a projected approval rate for the entity to approve the resource request, a projected risk for the entity to serve the resource request, and one or more multipliers applied to the projected approval rate and the projected risk; and recommending one of the plurality of entities to serve the resource request for the user based on the corresponding score, wherein the one or more multipliers are obtained by solving an optimization model constructed based on historical data collected from a previous period of time, the historical data comprising approval rates projected for the plurality of entities during the previous period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

Abstract: Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variables of each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating risk are provided. One of the methods includes: obtaining a plurality of transactions each comprising a plurality of data dimensions, wherein some of the plurality of transactions are labeled as risky transactions and some of the plurality of transactions are labeled as safe transactions; obtaining at least one of the plurality of data dimensions as an output space and the plurality of data dimensions other than the at least one data dimension as an input space; initializing a first mapping from the input space to a latent space and a second mapping from the latent space to the output space, wherein the first mapping comprises mapping the input space to the latent space according to an inverse of a generalized linear model; and optimizing the first mapping and the second mapping to generate a Bezier surface.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system that includes a number of individual solvers. The method includes: receiving data representing K global constraints and L local constraints of the KP; decomposing the KP into N sub-problems using K dual multipliers, each of the N sub-problems corresponding to a respective one of the N users and subject to the L local constraints w.r.t. the corresponding user, wherein N is in an order of billions or larger; determining the number of individual solvers for solving the N sub-problems; distributing the N sub-problems among the number of individual solvers; and solving the KP by the distributed computing system by performing two or more iterations.

Abstract: Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variables of each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system that includes a number of individual solvers. The method includes: receiving data representing K global constraints and L local constraints of the KP; decomposing the KP into N sub-problems using K dual multipliers, each of the N sub-problems corresponding to a respective one of the N users and subject to the L local constraints w.r.t. the corresponding user, wherein N is in an order of billions or larger; determining the number of individual solvers for solving the N sub-problems; distributing the N sub-problems among the number of individual solvers; and solving the KP by the distributed computing system by performing two or more iterations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for risk detection. One exemplary method may comprise: obtaining a first subset of a plurality of risk-detection rules, the first subset being associated with a first coverage score; constructing, based on the first subset, a lower-bound data mapping that outputs an approximate coverage score for an input subset; and constructing, based on the first subset, an upper-bound data mapping comprising a set of parameters; and generating a third subset of the plurality of risk-detection rules; and in response to the first coverage score exceeding the third coverage score, selecting rules in the first subset for risk-detection on a new transaction.

Abstract: Disclosed methods, systems, and apparatus, include computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system. The method includes: receiving data representing K global constraints and L local constraints of the KP; transforming the KP into a dual problem using K dual multipliers; decomposing the dual problem into N sub-problems; performing two or more iterations in solving the dual problem, wherein in one iteration, for each dual multiplier corresponding to a global constraint corresponding to a pool: determining an updated dual multiplier for the global constraint corresponding to the pool to be a non-negative threshold; and computing M decision variables of each of the N users corresponding to the updated dual multiplier in solving each of the N sub-problems corresponding to the each of the N users.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing allocation of M resources among N users into K pools by solving a knapsack problem (KP) using a distributed computing system that includes a number of individual solvers. The method includes: receiving data representing K global constraints and L local constraints of the KP; decomposing the KP into N sub-problems using K dual multipliers, each of the N sub-problems corresponding to a respective one of the N users and subject to the L local constraints w.r.t. the corresponding user, wherein N is in an order of billions or larger; determining the number of individual solvers for solving the N sub-problems; distributing the N sub-problems among the number of individual solvers; and solving the KP by the distributed computing system by performing two or more iterations.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing allocating M resources subject to L constraints. The method includes: receiving data representing L constraints, wherein each of the L constraints corresponds to a subset of M resources and restricts a respective maximum number C of resources to be selected among the subset of the M resources, wherein the L constraints has a hierarchal structure; determining a topological ordering of the L constraints; selecting all the M resources as an initial selection; removing resources from the initial selection by traversing each constraint in the topological ordering of the L constraints; and allocating the selected resources after traversing all the L constraints in the topological ordering of the L constraints.