no code implementations • 24 Feb 2024 • Erdem Biyik, Nima Anari, Dorsa Sadigh
Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time.
no code implementations • 17 Jan 2024 • Nima Anari, Sinho Chewi, Thuy-Duong Vuong
For our main application, we show how to combine the TV distance guarantees of our algorithms with prior works and obtain RNC sampling-to-counting reductions for families of discrete distribution on the hypercube $\{\pm 1\}^n$ that are closed under exponential tilts and have bounded covariance.
no code implementations • 6 Apr 2022 • Nima Anari, Yang P. Liu, Thuy-Duong Vuong
We even improve the state of the art for obtaining a single sample from determinantal point processes, from the prior runtime of $\widetilde{O}(\min\{nk^2, n^\omega\})$ to $\widetilde{O}(nk^{\omega-1})$.
no code implementations • 27 Sep 2021 • Vivek Myers, Erdem Biyik, Nima Anari, Dorsa Sadigh
However, expert feedback is often assumed to be drawn from an underlying unimodal reward function.
no code implementations • 10 Feb 2021 • Nima Anari, Thuy-Duong Vuong
We show a connection between sampling and optimization on discrete domains.
no code implementations • 4 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
no code implementations • 17 Dec 2020 • Nima Anari, Nathan Hu, Amin Saberi, Aaron Schild
For several well-studied combinatorial structures, counting can be reduced to the computation of a determinant, which is known to be in NC [Csa75].
Point Processes Data Structures and Algorithms Combinatorics Probability
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.
no code implementations • 15 Apr 2020 • Nima Anari, Kuikui Liu, Shayan Oveis Gharan, Cynthia Vinzant
For a matroid of rank $k$ on a ground set of $n$ elements, or more generally distributions associated with log-concave polynomials of homogeneous degree $k$ on $n$ variables, we show that the down-up random walk, started from an arbitrary point in the support, mixes in time $O(k\log k)$.
Data Structures and Algorithms Discrete Mathematics Probability
no code implementations • 6 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).
1 code implementation • 19 Jun 2019 • Erdem Biyik, Kenneth Wang, Nima Anari, Dorsa Sadigh
While active learning methods attempt to tackle this issue by labeling only the data samples that give high information, they generally suffer from large computational costs and are impractical in settings where data can be collected in parallel.
no code implementations • NeurIPS 2018 • Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos H. Papadimitriou, Amin Saberi, Santosh Vempala
We give an application to recovering assemblies of neurons.
no code implementations • 2 Jul 2018 • Nima Anari, Shayan Oveis Gharan, Cynthia Vinzant
We give a deterministic polynomial time $2^{O(r)}$-approximation algorithm for the number of bases of a given matroid of rank $r$ and the number of common bases of any two matroids of rank $r$.
Data Structures and Algorithms Information Theory Combinatorics Information Theory Probability
no code implementations • 16 Feb 2016 • Nima Anari, Shayan Oveis Gharan, Alireza Rezaei
Strongly Rayleigh distributions are natural generalizations of product and determinantal probability distributions and satisfy strongest form of negative dependence properties.