3 papers
cs.LG2025
A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems
Xavier Gonzalez, E. Kelly Buchanan, Hyun Dong Lee +6
Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes…
stat.CO2025
Parallelizing MCMC Across the Sequence Length
David M. Zoltowski, Skyler Wu, Xavier Gonzalez +2
Markov chain Monte Carlo (MCMC) methods are foundational algorithms for Bayesian inference and probabilistic modeling. However, most MCMC algorithms are inherently sequential and t…
math.OC2025
Predictability Enables Parallelization of Nonlinear State Space Models
Xavier Gonzalez, Leo Kozachkov, David M. Zoltowski +2
The rise of parallel computing hardware has made it increasingly important to understand which nonlinear state space models can be efficiently parallelized. Recent advances like DE…