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Skyler Wu

2 accepted papers

2025

Parallelizing MCMC Across the Sequence Length

NeurIPS 2025poster

Markov chain Monte Carlo (MCMC) methods are foundational algorithms for Bayesian inference and probabilistic modeling. However, most MCMC algorithms are inherently sequential and their time complexity scales linearly with the sequence length. Previous work on adapting MCMC to modern hardware has the…

Cited by 0SourceScholar
2024

Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling

NeurIPS 2024poster

Online learning methods, like the seminal Passive-Aggressive (PA) classifier, are still highly effective for high-dimensional streaming data, out-of-core processing, and other throughput-sensitive applications. Many such algorithms rely on fast adaptation to individual errors as a key to their conve…