13 citations · 15 across the 5 of their papers we have counts for
5 papers
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
Feynman Liang, Nimar Arora, Nazanin Tehrani +3
In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-sou…
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora +9
We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and eva…
Uncertainty Estimation For Community Standards Violation In Online Social Networks
Narjes Torabi, Nimar S. Arora, Emma Yu +3
Online Social Networks (OSNs) provide a platform for users to share their thoughts and opinions with their community of friends or to the general public. In order to keep the platf…
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods
Nimar S. Arora, Nazanin Khosravani Tehrani, Kinjal Divesh Shah +7
Single-site Markov Chain Monte Carlo (MCMC) is a variant of MCMC in which a single coordinate in the state space is modified in each step. Structured relational models are a good c…
Gibbs Sampling in Open-Universe Stochastic Languages
Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth +1
Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty. While such cases arise in a wide ran…