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Found 5 papers, 1 papers with code
Learning Certifiably Optimal Rule Lists for Categorical Data
We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.
Patterns of Scalable Bayesian Inference
Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge.
Variational consensus Monte Carlo
Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions.
Accelerating MCMC via Parallel Predictive Prefetching
We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms.
Attribute-Efficient Evolvability of Linear Functions
In a seminal paper, Valiant (2006) introduced a computational model for evolution to address the question of complexity that can arise through Darwinian mechanisms.
