Search Results for author: Nima Anari

Found 14 papers, 1 papers with code

Batch Active Learning of Reward Functions from Human Preferences

no code implementations24 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.

Active Learning Point Processes

Fast parallel sampling under isoperimetry

no code implementations17 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.

Point Processes

Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence

no code implementations6 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 $\widetilde{O}(nk^{\omega-1})$.

Point Processes

Learning Multimodal Rewards from Rankings

no code implementations27 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.

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

Sampling Arborescences in Parallel

no code implementations17 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

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.

Log-Concave Polynomials IV: Approximate Exchange, Tight Mixing Times, and Near-Optimal Sampling of Forests

no code implementations15 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

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).

Batch Active Learning Using Determinantal Point Processes

1 code implementation19 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.

Active Learning Point Processes

Log-Concave Polynomials I: Entropy and a Deterministic Approximation Algorithm for Counting Bases of Matroids

no code implementations2 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

Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes

no code implementations16 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.

Open-Ended Question Answering Point Processes

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