Minimax sample complexity for uniform L_infty estimation is Theta(n^{d+1}) for degree-d polynomials and Theta(ns^2) for s-sparse Fourier-Walsh polynomials under noise, exceeding noiseless rates by factors of n and s.
Hoffman, Troy David Loeffler, and Subramanian Sankaranarayanan
2 Pith papers cite this work. Polarity classification is still indexing.
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Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials
Minimax sample complexity for uniform L_infty estimation is Theta(n^{d+1}) for degree-d polynomials and Theta(ns^2) for s-sparse Fourier-Walsh polynomials under noise, exceeding noiseless rates by factors of n and s.
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Term-Centric Hierarchy Induction from Heterogeneous Corpora
A term-centric framework uses automatic term extraction to align heterogeneous document collections into a shared space and builds hierarchies by combining domain priors with clustering, outperforming document-level baselines on a 1M+ document English-German benchmark.