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Sample Complexity Bounds for Differentially Private Learning Cseweb Ucsd Form
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People also ask
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Which computational learning theory analyzes the sample complexity and computational complexity?
Computational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis language and the ease of learning. -
What is the difference between sample complexity computational complexity and mistake bound?
– High sample complexity implies high computational complexity, since learner at least needs to read the input data. Mistake Bound: Learning incrementally, how many training examples will the learner misclassify before constructing a highly accurate concept. -
What is the sample complexity of differentially private learning?
They showed that the sample complexity1 of differentially privately learning a concept class C over domain X, denoted by SCDP(C), is O(log |C|) and left open the natural question of whether SCDP(C) = O(VC(C)). Note that the gap between these two measures can be as large as (and no more than) log(|X|). -
How do you measure model complexity?
Model complexity in Neural Network is usually measured in terms of the number of hidden units and layers and their connectivity, the form of activation functions, and parameters of the learning algorithm itself. Increasing the number and size of layers used in a neural network model, increases model complexity. -
What is sample complexity of a learning problem?
The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. -
What is the sample complexity of a hypothesis class?
The sample complexity is a function of the accuracy (ϵ) and confidence (δ) parameters. It also depends on properties of the hypothesis class H – for example, for a finite class we showed that the sample complexity depends on log the size of H.
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