Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Authors: Difan Zou, Pan Xu, Quanquan Gu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments on synthetic and real-world datasets validate our theory and demonstrate the superiority of SRVR-HMC. |
| Researcher Affiliation | Academia | Difan Zou Department of Computer Science University of California, Los Angeles Los Angeles, CA 90095 knowzou@cs.ucla.edu Pan Xu Department of Computer Science University of California, Los Angeles Los Angeles, CA 90095 panxu@cs.ucla.edu Quanquan Gu Department of Computer Science University of California, Los Angeles Los Angeles, CA 90095 qgu@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1 Stochastic Recursive Variance-Reduced gradient HMC (SRVR-HMC) |
| Open Source Code | No | The paper does not include any explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We compare the performance of SRVR-HMC with all the baseline algorithms on MEG dataset4, which consists of 17730 time-points in 122 channels. 4http://research.ics.aalto.fi/ica/eegmeg/MEG_data.html |
| Dataset Splits | No | The paper mentions: 'we extract two subset with sizes n = 500 and n = 5000 from the original dataset for training, and regard the rest 12730 examples as test dataset.' This describes training and test sets but does not specify a separate validation set split. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies, libraries, or solvers with their version numbers that are needed to replicate the experiments. |
| Experiment Setup | Yes | Specifically, we run SRVR-HMC for 10^4 data passes, and use the last 10^5 iterates to visualize the estimated distribution, where the batch size, minibatch size and epoch length are set to be B0 = n, B = 1 and L = n respectively. where the batch size, minibatch size and epoch length are set to be B0 = n/5, B = 10 and L = B0/B, and the rest hyper parameters are tuned to achieve the best performance. |