Christopher Nemeth

1. Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
   2024/02/01 by Theodore Papamarkou, Papamarkou, Theodore, Maria Skoularidou +48 · 1 voice · 15 citations
   Computer Science · #Anomaly Detection Techniques and Applications #Machine Learning and Data Classification #cs.LG #stat.ML
2. Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
   2013/06/04 by Christopher Nemeth, Nemeth, Christopher, Paul Fearnhead +3 · 3 citations
   Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
3. Latent Space Modelling of Hypergraph Data
   2019/09/01 by Kathryn Turnbull, Simon Lunagómez, Turnbull, Kathryn +5 · 2 citations
   Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Computer Science · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Topological and Geometric Data Analysis
4. GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
   2018/12/21 by Jamie Fairbrother, Christopher Nemeth, Fairbrother, Jamie +7 · 1 citation
   Computer Science · #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML)
5. SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy
   2022/08/08 by Callum Vyner, Vyner, Callum, Christopher Nemeth +3 · 1 citation
   Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference
6. Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
   2024/06/04 by Daniel Dodd, Dodd, Daniel, Louis Sharrock +3 · 1 citation
   Computer Science · Mathematics · #Neural Networks and Applications #Stochastic Gradient Optimization Techniques #Statistical Methods and Inference