Polynomial-time algorithm recovers the conditional-independence graph of a d-sparse GGM from one Glauber trajectory with length independent of mixing time.
The Thirty Seventh Annual Conference on Learning Theory , pages=
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Near-linear time algorithm for robust regression under Gaussian covariates achieves O(sqrt(ε κ)) error with Õ(d/ε⁴) samples when ε κ ≲ 1, plus SQ and low-degree lower bounds.
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Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
Polynomial-time algorithm recovers the conditional-independence graph of a d-sparse GGM from one Glauber trajectory with length independent of mixing time.
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On efficient robust regression with subquadratic samples
Near-linear time algorithm for robust regression under Gaussian covariates achieves O(sqrt(ε κ)) error with Õ(d/ε⁴) samples when ε κ ≲ 1, plus SQ and low-degree lower bounds.