Machine-learning techniques for monotonic neural networks
First Claim
1. A method that includes one or more processing devices performing operations comprising:
- training a neural network model for computing a risk indicator from predictor variables, wherein the neural network model is a memory structure comprising nodes connected via one or more layers, wherein training the neural network model to generate a trained neural network model comprises;
accessing training vectors having elements representing training predictor variables and training outputs, wherein a particular training vector comprises (i) particular values for the predictor variables, respectively, and (ii) a particular training output corresponding to the particular values, andperforming iterative adjustments of parameters of the neural network model to minimize a loss function of the neural network model subject to a path constraint, the path constraint requiring a monotonic relationship between (i) values of each predictor variable from the training vectors and (ii) the training outputs of the training vectors, wherein one or more of the iterative adjustments comprises adjusting the parameters of the neural network model so that a value of a modified loss function in a current iteration is smaller than the value of the modified loss function in another iteration, and wherein the modified loss function comprises the loss function of the neural network model and the path constraint;
receiving, from a remote computing device, a risk assessment query for a target entity;
computing, responsive to the risk assessment query, an output risk indicator for the target entity by applying the trained neural network model to predictor variables associated with the target entity; and
transmitting, to the remote computing device, a responsive message including the output risk indicator, wherein the output risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.
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Abstract
In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.
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Citations
19 Claims
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1. A method that includes one or more processing devices performing operations comprising:
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training a neural network model for computing a risk indicator from predictor variables, wherein the neural network model is a memory structure comprising nodes connected via one or more layers, wherein training the neural network model to generate a trained neural network model comprises; accessing training vectors having elements representing training predictor variables and training outputs, wherein a particular training vector comprises (i) particular values for the predictor variables, respectively, and (ii) a particular training output corresponding to the particular values, and performing iterative adjustments of parameters of the neural network model to minimize a loss function of the neural network model subject to a path constraint, the path constraint requiring a monotonic relationship between (i) values of each predictor variable from the training vectors and (ii) the training outputs of the training vectors, wherein one or more of the iterative adjustments comprises adjusting the parameters of the neural network model so that a value of a modified loss function in a current iteration is smaller than the value of the modified loss function in another iteration, and wherein the modified loss function comprises the loss function of the neural network model and the path constraint; receiving, from a remote computing device, a risk assessment query for a target entity; computing, responsive to the risk assessment query, an output risk indicator for the target entity by applying the trained neural network model to predictor variables associated with the target entity; and transmitting, to the remote computing device, a responsive message including the output risk indicator, wherein the output risk indicator is usable for controlling access to one or more interactive computing environments by the target entity. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to; train a neural network model for computing a risk indicator from predictor variables, wherein the neural network model is a memory structure comprising nodes connected via one or more layers, wherein training the neural network model to generate a trained neural network model comprises; access training vectors having elements representing training predictor variables and training outputs, wherein a particular training vector comprises (i) particular values for the predictor variables, respectively, and (ii) a particular training output corresponding to the particular values, and perform iterative adjustments of parameters of the neural network model to minimize a loss function of the neural network model subject to a path constraint, the path constraint requiring a monotonic relationship between (i) values of each predictor variable from the training vectors and (ii) the training outputs of the training vectors, wherein one or more of the iterative adjustments comprises adjusting the parameters of the neural network model so that a value of a modified loss function in a current iteration is smaller than the value of the modified loss function in another iteration, and wherein the modified loss function comprises the loss function of the neural network model and the path constraint; compute, responsive to a risk assessment query for a target entity received from a remote computing device, an output risk indicator for the target entity by applying the trained neural network model to predictor variables associated with the target entity; and transmit, to the remote computing device, a responsive message including the output risk indicator, wherein the output risk indicator is usable for controlling access to one or more interactive computing environments by the target entity. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operations, the operations comprising:
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training a neural network model for computing a risk indicator from predictor variables, wherein the neural network model is a memory structure comprising nodes connected via one or more layers, wherein training the neural network model to generate a trained neural network comprises; accessing training vectors having elements representing training predictor variables and training outputs, wherein a particular training vector comprises (i) particular values for the predictor variables, respectively, and (ii) a particular training output corresponding to the particular values, and performing iterative adjustments of parameters of the neural network model to minimize a loss function of the neural network model subject to a path constraint, the path constraint requiring a monotonic relationship between (i) values of each predictor variable from the training vectors and (ii) the training outputs of the training vectors, wherein one or more of the iterative adjustments comprises adjusting the parameters of the neural network model so that a value of a modified loss function in a current iteration is smaller than the value of the modified loss function in another iteration, and wherein the modified loss function comprises the loss function of the neural network model and the path constraint; computing, responsive to a risk assessment query for a target entity received from a remote computing device, an output risk indicator for the target entity by applying the trained neural network model to predictor variables associated with the target entity; and transmitting, to the remote computing device, a responsive message including the output risk indicator, wherein the output risk indicator is usable for controlling access to one or more interactive computing environments by the target entity. - View Dependent Claims (16, 17, 18, 19)
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Specification