Search Results for author: Kirankumar Shiragur

Found 5 papers, 1 papers with code

Fractionally Log-Concave and Sector-Stable Polynomials: Counting Planar Matchings and More

no code implementations4 Feb 2021 Yeganeh Alimohammadi, Nima Anari, Kirankumar Shiragur, Thuy-Duong Vuong

While perfect matchings on planar graphs can be counted exactly in polynomial time, counting non-perfect matchings was shown by [Jer87] to be #P-hard, who also raised the question of whether efficient approximate counting is possible.

Point Processes Data Structures and Algorithms Combinatorics Probability

Instance Based Approximations to Profile Maximum Likelihood

no code implementations NeurIPS 2020 Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford

In this paper we provide a new efficient algorithm for approximately computing the profile maximum likelihood (PML) distribution, a prominent quantity in symmetric property estimation.

The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood

no code implementations6 Apr 2020 Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford

For each problem we provide polynomial time algorithms that given $n$ i. i. d.\ samples from a discrete distribution, achieve an approximation factor of $\exp\left(-O(\sqrt{n} \log n) \right)$, improving upon the previous best-known bound achievable in polynomial time of $\exp(-O(n^{2/3} \log n))$ (Charikar, Shiragur and Sidford, 2019).

A General Framework for Symmetric Property Estimation

1 code implementation NeurIPS 2019 Moses Charikar, Kirankumar Shiragur, Aaron Sidford

In this paper we provide a general framework for estimating symmetric properties of distributions from i. i. d.

Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation

no code implementations21 May 2019 Moses Charikar, Kirankumar Shiragur, Aaron Sidford

Generalizing work of Acharya et al. 2016 on the utility of approximate PML we show that our algorithm provides a nearly linear time universal plug-in estimator for all symmetric functions up to accuracy $\epsilon = \Omega(n^{-0. 166})$.

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