Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models
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We establish optimal Statistical Query (SQ) lower bounds for robustly learning certain families of discrete high-dimensional distributions. In particular, we show that no efficient SQ algorithm with access to an ϵ-corrupted binary product distribution can learn its mean within ℓ_2-error o(ϵ√(log(1/ϵ))). Similarly, we show that no efficient SQ algorithm with access to an ϵ-corrupted ferromagnetic high-temperature Ising model can learn the model to total variation distance o(ϵlog(1/ϵ)). Our SQ lower bounds match the error guarantees of known algorithms for these problems, providing evidence that current upper bounds for these tasks are best possible. At the technical level, we develop a generic SQ lower bound for discrete high-dimensional distributions starting from low dimensional moment matching constructions that we believe will find other applications. Additionally, we introduce new ideas to analyze these moment-matching constructions for discrete univariate distributions.
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