Technologies are generally described for methods and systems in a machine learning environment. In some examples, a method may include retrieving training data from a memory. The training data may include training inputs and training labels. The methods may further include determining a set of datasets based on the training inputs. The methods may further include determining a set of out of sample errors based on the training inputs and based on test data. Each out of sample error may correspond to a respective dataset in the set of datasets. The methods may further include generating alternative distribution data based on the set of out of sample errors. The alternative distribution data may be used to determine weights to be applied to the training data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method in a machine learning environment, the method comprising, by a computing device: determining a set of datasets in an input space based on training data for a machine learning module, wherein the training data comprise training inputs and training labels in the input space; determining a set of out of sample errors based on the training inputs, wherein each out of sample error corresponds to a respective dataset in the set of datasets; and generating alternative distribution data for the machine learning module based on the set of out of sample errors. 2. The method of claim 1, wherein generating the alternative distribution data comprises: determining an objective function based on the set of out of sample errors; determining a set of parameters of the objective function; and generating the alternative distribution data with use of the set of parameters. 3. The method of claim 2, wherein determining the set of parameters of the objective function comprises applying a convex optimization technique to the objective function. 4. The method of claim 2, wherein the set of parameters are parameters which minimizes a value of the objective function. 5. The method of claim 1, further comprising generating weights for the training data based on the alternative distribution data. 6. The method of claim 1, wherein determining the set of out of sample errors comprises: applying a transform to the training inputs to produce transformed inputs; determining an error function based on the transformed inputs, and based on test inputs for the machine learning module; and determining the set of out of sample errors based on the error function. 7. The method of claim 6, further comprising combining a noise parameter with the transformed inputs and the test inputs to determine the error function. 8. A system effective to generate alternative distribution data in a machine learning environment, the system comprising: a processing module configured to determine a set of datasets in an input space based on training data for a machine learning module, wherein the training data comprise training inputs and training labels in the input space; an evaluation module configured to be in communication with the processing module, the evaluation module being configured to determine a set of out of sample errors based on the training inputs, wherein each out of sample error corresponds to a respective dataset in the set of datasets; and the processing module being further configured to generate the alternative distribution data based on the set of out of sample errors. 9. The system of claim 8, further comprising an optimization module configured to be in communication with the memory, the processing module, and the evaluation module, the optimization module being configured to: determine an objective function based on the set of out of sample errors; and determine a set of parameters of the objective function; and send the set of parameters to the processing module; wherein the processing module is further configured to generate the alternative distribution data with use of the set of parameters. 10. The system of claim 9, wherein the optimization module is further configured to apply a convex optimization technique to the objective function to determine the set of parameters. 11. The system of claim 9, wherein the set of parameters are parameters which minimizes a value of the objective function. 12. The system of claim 8, wherein the processing module is further configured to generate weights for the training data based on the alternative distribution data. 13. The system of claim 8, wherein the evaluation module is further configured to: apply a transform to the training inputs to produce transformed inputs; determine an error function based on the transformed inputs and based on the test inputs for the machine learning module; and determine the set of out of sample errors based on the error function. 14. The system of claim 13, wherein the error function further comprises a noise parameter. 15. A method to generate alternative distribution data in a machine learning environment, the method comprising, by a first device: receiving, from a second device, training data for a machine learning module, wherein the training data comprises training inputs and training labels in an input space; determining a set of datasets based on the training inputs; receiving test inputs for the machine learning module from the second device; determining a set of out of sample errors based on the training inputs and based on the test inputs, wherein each out of sample error corresponds to a respective dataset in the set of datasets; and generating the alternative distribution data based on the set of out of sample errors. 16. The method of claim 15, wherein generating the alternative distribution data comprises: determining an objective function based on the set of out of sample errors; and determining a set of parameters of the objective function; and generating the alternative distribution with use of the set of parameters. 17. The method of claim 16, wherein determining the set of parameters comprises applying a convex optimization technique to the objective function. 18. The method of claim 16, wherein the set of parameters are parameters which minimizes a value of the objective function. 19. The method of claim 15, further comprising generating weights for the training data based on the alternative distribution data. 20. The method of claim 15, wherein determining the set of out of sample errors comprises: applying a transform to the training inputs to produce transformed inputs; determining an error function based on the transformed inputs and based on the test inputs; and determining the set of out of sample errors based on the error function.