## A problem about the relation between 1-oracle and 2-oracle PAC model

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This problem is about two-oracle variant of the PAC model. Assume that positive and negative examples are now drawn from two separate distributions $$\mathcal{D}_{+}$$ and $$\mathcal{D}_{-} .$$ For an accuracy $$(1-\epsilon),$$ the learning algorithm must find a hypothesis $$h$$ such that: $$\underset{x \sim \mathcal{D}_{+}}{\mathbb{P}}[h(x)=0] \leq \epsilon \text { and } \underset{x \sim \mathcal{D}_{-}}{\mathbb{P}}[h(x)=1] \leq \epsilon$$

Thus, the hypothesis must have a small error on both distributions. Let $$\mathcal{C}$$ be any concept class and $$\mathcal{H}$$ be any hypothesis space. Let $$h_{0}$$ and $$h_{1}$$ represent the identically 0 and identically 1 functions, respectively. Prove that $$\mathcal{C}$$ is efficiently PAC-learnable using $$\mathcal{H}$$ in the standard (one-oracle) PAC model if and only if it is efficiently PAC-learnable using $$\mathcal{H} \cup\left\{h_{0}, h_{1}\right\}$$ in this two-oracle PAC model.

However, I wonder if the problem is correct. In the official solution, when showing that 2-oracle implies 1-oracle, the author returns $$h_0$$ and $$h_1$$ when the distribution is too biased towards positive or negative examples. However, in the problem, it is required that only in 2-oracle case we can return $$h_0$$ and $$h_1$$. Therefore, in this too-biased case, it seems that there may not exist a 'good' hypothesis at all.

Is this problem wrong? Or I make some mistake somewhere?

Can we decompose probabilities like done in the 'so called' proof? From $D$ to $D^+$ and $D^-$. – DuttaA – 2020-03-17T22:48:33.367