Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Authors: Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our claims with experimental studies on both toy and real-world applications, including Approximate Bayesian Computation and exact-approximate MCMC. |
| Researcher Affiliation | Academia | Gatsby Unit University College London +Department of Statistics University of Oxford o School of Mathematics University of Bristol |
| Pseudocode | Yes | Algorithm 1 Kernel Hamiltonian Monte Carlo Pseudo-code |
| Open Source Code | Yes | All code can be found at https://github.com/karlnapf/kernel_hmc |
| Open Datasets | Yes | We next apply KMC to sample from the marginal posterior over hyper-parameters of a Gaussian Process Classification (GPC) model on the UCI Glass dataset [24]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It describes tuning parameters but does not specify a validation set for data partitioning. |
| Hardware Specification | No | The paper mentions that "All samplers took 1h time", but does not specify any hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies. |
| Experiment Setup | Yes | We tuned the scaling of KAMH and RW to achieve 23% acceptance. We set HMC parameters to achieve 80% acceptance and then used the same parameters for KMC. We ran all samplers for 2000+200 iterations from a random start point, discarded the burn-in and computed acceptance rates, the norm of the empirical mean ˆE[x] , and the minimum effective sample size (ESS) across dimensions. For KMC we pick randomly between 1 and 10 leapfrog steps, chosen uniformly from [0.01, 0.1], a standard Gaussian momentum, and a kernel tuned by cross-validation |