Decision-making under selective labels
View Patent ↗A computer-implemented method of decision-making using selective labels, includes receiving a conditional success probability value of a feature associated with an entity. A confidence value of the received success probability value is received. A parameter value that is a trade-off between a short-term learning and a long-term utility is selected. A decision is rendered to accept or reject the feature associated with the entity according to a machine learning policy.
1 . A computer-implemented method of automatic decision-making using selective labels, the method comprising:
receiving, at a success probability model of a computing device, a feature vector associated with an entity and generating, via the success probability model analyzing the feature vector, a conditional success probability value representing a likelihood of success of a feature associated with the entity if the entity is accepted;
receiving, at a confidence model of the computing device, a sample size associated with prior accepted entities having similar features and generating, via the confidence model analyzing the sample size, a confidence value associated with the conditional success probability value;
determining, at a discount factor model of the computing device, a tuning parameter value that is <1 and that represents a trade-off between a short-term learning cost and a long-term utility;
rendering a decision, using a policy model of the computing device, to accept the entity based on a threshold function of the conditional success probability value, the confidence value, and the tuning parameter value; and
iteratively updating the success probability model and the confidence model using an observed outcome that results from accepting the entity.
2 . The computer-implemented method of claim 1 , wherein:
the machine learning policy for rendering the decision comprises an optimal homogeneous policy; and
the feature associated with the entity is non-distinguishable from a population of other entities.
3 . The computer-implemented method of claim 1 , wherein the machine learning policy for rendering the decision comprises a homogeneous policy for rendering the decision to accept based on the feature associated with the entity.
4 . The computer-implemented method of claim 1 , wherein the machine learning policy for rendering the decision comprises a finite-domain case policy used for rendering the decision to accept based on the feature associated with the entity.
5 . The computer-implemented method of claim 1 , wherein the machine learning policy for rendering the decision comprises an infinite-domain case policy used for rendering the decision to accept the feature associated with the entity.
6 . The computer-implemented method of claim 1 , further comprising updating the policy model based on the observed outcome that results from accepting the entity.
7 . The computer-implemented method of claim 1 , further comprising training a machine learning model to render the decision to accept the feature associated with the entity.
8 . The computer-implemented method of claim 1 , wherein the entity includes multiple features, and the computer-implemented method further comprises training a machine learning model to render the decision to accept based on two or more of the multiple features associated with the entity.
9 . The computer-implemented method of claim 1 , wherein the machine learning policy is based on the conditional success probability value and the confidence value.
10 . A computing device configured for decision-making using selective labels, the computing device comprising:
a processor,
a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising:
receiving, at a success probability model of a computing device, a feature vector associated with an entity and generating, via the success probability model analyzing the feature vector, a conditional success probability value representing a likelihood of success of a feature associated with the entity if the entity is accepted;
receiving, at a confidence model of the computing device, a sample size associated with prior accepted entities having similar features and generating, via the confidence model analyzing the sample size, a confidence value associated with the conditional success probability value;
determining, at a discount factor model of the computing device, a tuning parameter value that is <1 and that represents a trade-off between a short-term learning cost and a long-term utility;
rendering a decision, using a policy model of the computing device, to accept the entity based on a threshold function of the conditional success probability value, the confidence value, and the tuning parameter value; and
iteratively updating the success probability model and the confidence model using an observed outcome that results from accepting the entity.
11 . The computing device of claim 10 , wherein the instructions cause the processor to perform an additional act of training a machine learning model to render the decision to accept the feature associated with the entity.
12 . The computing device of claim 10 , wherein the instructions cause the processor to perform an additional act of training a machine learning model to render the decision to accept based on two or more of the features associated with the entity.
13 . The computing device of claim 10 , wherein the instructions cause the processor to perform an additional act of rendering the decision to accept based on an optimal homogeneous policy.
14 . A computing device configured to perform decision-making using selective labels, the computing device comprising:
one or more processors including a dialog processor configured to process extracted text from a plurality of participants of a collaborative query;
a memory coupled to the one or more processors;
a plurality of models configured in the one or more processors, the plurality of models comprising:
a success probability model configured to provide a conditional success probability value representing an empirical success rate of a feature associated with an entity;
a confidence model configured to provide a confidence value of the conditional success probability value, based at least on a sample size of entities;
a discount factor model configured to determine a tuning parameter value that is <1 and that represents a trade-off between a short-term learning cost and a long-term utility; and
a policy model configured to render a decision to accept the feature associated with the entity based on a threshold function of the conditional success probability value, the confidence value, and the tuning parameter value; and
iteratively update the success probability model and the confidence model with a resultant outcome when the decision is to accept.
15 . The computing device according to claim 14 , wherein the success probability model and the confidence model each comprise a predictive model.
16 . The computing device according to claim 14 , wherein the success probability model is configured for automatically rendering decisions for dispensing a requested pharmaceutical or biological treatment.
17 . The computing device according to claim 14 , wherein the success probability model is configured for rendering decisions regarding suspending an operating license.