Sample NSF Fellowship Proposal Essays
نویسندگان
چکیده
منابع مشابه
Testing k-Modal Distributions: Optimal Algorithms via Reductions
We give highly efficient algorithms, and almost matching lower bounds, for a range of basic statistical problems that involve testing and estimating the L1 (total variation) distance between two k-modal distributions p and q over the discrete domain {1, . . . , n}. More precisely, we consider the following four problems: given sample access to an unknown kmodal distribution p, TESTING IDENTITY ...
متن کاملFast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms
We study the problem of robustly learning multi-dimensional histograms. A d-dimensional function h : D → R is called a k-histogram if there exists a partition of the domain D ⊆ R into k axis-aligned rectangles such that h is constant within each such rectangle. Let f : D → R be a d-dimensional probability density function and suppose that f is OPT-close, in L1-distance, to an unknown k-histogra...
متن کاملPrivacy-Compatibility For General Utility Metrics
In this note, we present a complete characterization of the utility metrics that allow for non-trivial differential privacy guarantees. Department of Computer Science, Cornell University, Ithaca NY 14853. E-mail: {rdk,katrina}@cs.cornell.edu. Supported by NSF Award CCF-0643934, an Alfred P. Sloan Foundation Fellowship, a Microsoft Research New Faculty Fellowship, and a grant from the Air Force ...
متن کاملBayesian estimation from few samples: community detection and related problems
We propose an efficient meta-algorithm for Bayesian estimation problems that is based on low-degree polynomials, semidefinite programming, and tensor decomposition. The algorithm is inspired by recent lower bound constructions for sum-of-squares and related to the method of moments. Our focus is on sample complexity bounds that are as tight as possible (up to additive lower-order terms) and oft...
متن کاملLow-Rank Matrix Completion with Adversarial Missing Entries
We give an algorithm for completing an order-m symmetric low-rank tensor from its multilinear entries in time roughly proportional to the number of tensor entries. We apply our tensor completion algorithm to the problem of learning mixtures of product distributions over the hypercube, obtaining new algorithmic results. If the centers of the product distribution are linearly independent, then we...
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