Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Authors: Difan Zou, Pan Xu, Quanquan Gu
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data demonstrate the superior performance of our algorithm. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of California, Los Angeles, CA 90095, USA. Correspondence to: Quanquan Gu <qgu@cs.ucla.edu>. |
| Pseudocode | Yes | Algorithm 1 Stochastic Variance-Reduced Hamiltonian Monte Carlo (SVR-HMC) |
| Open Source Code | No | The paper does not provide a link to open-source code or state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | We use four binary classification datasets from Libsvm (Chang & Lin, 2011) and UCI machine learning repository (Lichman, 2013), which are summarized in Table 3. |
| Dataset Splits | No | Note that pima and mushroom do not have test data in their original version, and we split them into 50% for training and 50% for test. The paper does not specify a validation split for any of the datasets used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using 'Libsvm' but does not specify software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | Set u = 1/L, γ = 2, x0 = 0, v0 = 0 and = e O(1/ 1/( 1/3n2/3))... let m = n and = O /( 1d 1/2) 2/3/( 1/3d1/3n2/3)... we report the sample path average and discard the first 50 iterations as burn-in. It is worth noting that we observe similar convergence comparison of different algorithms for larger burn-in period (= 104). We run each algorithm 20 times and report the averaged results for comparison. In our experiment, we set σ2a = 1 and λ = 1, and conduct the normalization of the original data. |