R
Raaz Dwivedi
Researcher at University of California, Berkeley
Publications - 35
Citations - 603
Raaz Dwivedi is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Order (ring theory) & Computer science. The author has an hindex of 11, co-authored 29 publications receiving 420 citations. Previous affiliations of Raaz Dwivedi include Indian Institutes of Technology & Massachusetts Institute of Technology.
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Journal Article
Log-concave sampling: Metropolis-Hastings algorithms are fast
TL;DR: In this paper, the mixing time of the Metropolis-Adjusted Langevin Algorithm (MALA) was shown to be O(log(1/δ) √ log(1)/δ ) for strongly log-concave densities with condition number δ.
Proceedings Article
Log-concave sampling: Metropolis-Hastings algorithms are fast!
TL;DR: A non-asymptotic upper bound on the mixing time of the Metropolis-adjusted Langevin algorithm (MALA) is proved, and the gains of MALA over ULA for weakly log-concave densities are demonstrated.
Posted Content
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
TL;DR: This work provides a non-asymptotic upper bound on the mixing time of the Metropolized HMC with explicit choices of stepsize and number of leapfrog steps, and provides a general framework for sharpening mixing time bounds Markov chains initialized at a substantial distance from the target distribution over continuous spaces.
Journal ArticleDOI
Curating a COVID-19 Data Repository and Forecasting County-Level Death Counts in the United States
Nick Altieri,Rebecca L Barter,James S. Duncan,Raaz Dwivedi,Karl Kumbier,Xiao Li,Robert Netzorg,Briton Park,Chandan Singh,Yan Shuo Tan,Tiffany Tang,Yu Wang,Chao Zhang,Bin Yu +13 more
TL;DR: This paper presents the continuous curation of a large data repository containing COVID-19 information from a range of sources, and develops and combines multiple forecasts using ensembling techniques, resulting in an ensemble that achieves a coverage rate of more than 94% when averaged across counties for predicting cumulative recorded death counts two weeks in the future.
Posted Content
Log-concave sampling: Metropolis-Hastings algorithms are fast
TL;DR: In this paper, the mixing time of the Metropolis-Adjusted Langevin Algorithm (MALA) was shown to be O(log(1/δ) √ log(1)/δ ) for strongly log-concave densities with condition number δ.