d^{poly(ℓ/ε)} algorithms for exponential families under polynomial-approximable unknown truncation, including first results for arbitrary Gaussians; poly(d/ε) for halfspaces/rectangles.
Learning From Satisfy- ing Assignments Under Continuous Distributions
2 Pith papers cite this work. Polarity classification is still indexing.
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SPIDER transforms a stateful single-server PIR protocol into one that delivers two-server-like private retrieval functionality using only a standard single server at no extra deployment cost.
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Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
d^{poly(ℓ/ε)} algorithms for exponential families under polynomial-approximable unknown truncation, including first results for arbitrary Gaussians; poly(d/ε) for halfspaces/rectangles.
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SPIDER: Two Server Functionality for the Cost of Zero
SPIDER transforms a stateful single-server PIR protocol into one that delivers two-server-like private retrieval functionality using only a standard single server at no extra deployment cost.