Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  derive risk bounds for the randomized classifiers in Sample Compression setting where the classifier-specification utilizes two sources of information viz. the compression set and the message string. By extending the recently proposed Occam's Hammer principle to the data-dependent settings,  derive point-wise versions of the bounds on the stochastic sample compressed classifiers and also recover the corresponding classical PAC-Bayes bound.  further show how these compare favorably to the existing results.
['Mohak Shah']
I am writing a research paper on a idea Risk Bounds for Randomized Sample Compressed Classifiers
[{"from": "human", "value": "I am writing a research paper on a idea Risk Bounds for Randomized Sample Compressed Classifiers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  derive risk bounds for the randomized classifiers in Sample Compression setting where the classifier-specification utilizes two sources of information viz. the compression set and the message string. By extending the recently proposed Occam's Hammer principle to the data-dependent settings,  derive point-wise versions of the bounds on the stochastic sample compressed classifiers and also recover the corresponding classical PAC-Bayes bound.  further show how these compare favorably to the existing results."}]