Online sampling from log-concave distributions
Authors: Holden Lee, Oren Mangoubi, Nisheeth Vishnoi
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations, our algorithm achieves accuracy comparable to an algorithm specialized to logistic regression. |
| Researcher Affiliation | Academia | Holden Lee Duke University Oren Mangoubi Worcester Polytechnic Institute Nisheeth K. Vishnoi Yale University |
| Pseudocode | Yes | Algorithm 1 SAGA-LD ... Algorithm 2 Online SAGA-LD |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The data is generated as follows. First, N(0, Id), b N(0, 1) are randomly generated. For each 1 t T, a feature vector xt 2 Rd and output yt 2 {0, 1} are generated by xt,i Bernoulli yt Bernoulli(φ( >xt + b)), (3) where the sparsity is s = 5 in our simulations, and φ(x) = 1 1+e x is the logistic function. We chose xt 2 {0, 1}d because in applications, features are often indicators. (The paper describes a synthetic dataset generation process but does not make the dataset itself publicly available or refer to a standard public dataset.) |
| Dataset Splits | No | The paper describes how samples were collected and simulations replicated ( |
| Hardware Specification | Yes | The experiments were run on Fujitsu CX2570 M2 servers with dual, 14-core 2.4GHz Intel Xeon E5 2680 v4 processors with 384GB RAM running the Springdale distribution of Linux. |
| Software Dependencies | No | The paper mentions 'running the Springdale distribution of Linux' but does not specify any programming languages, libraries, or other software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | The step size at epoch t is 0.1 1+0.5t for MALA, 0.01 1+0.5t for SGLD, and 0.05 1+0.5t for online SAGA-LD. A smaller step size must be used with SGLD because of the increased variance. For MALA, a larger step size can be used because the Metropolis-Hastings acceptance step ensures the stationary distribution is correct. The batch size for SGLD and online SAGA-LD is 64. |