Online Convex Optimization with Unbounded Memory
Authors: Raunak Kumar, Sarah Dean, Robert Kleinberg
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present some simple simulation experiments. |
| Researcher Affiliation | Academia | Raunak Kumar Department of Computer Science Cornell University Ithaca, NY 14853 EMAIL Sarah Dean Department of Computer Science Cornell University Ithaca, NY 14853 EMAIL Robert Kleinberg Department of Computer Science Cornell University Ithaca, NY 14853 EMAIL |
| Pseudocode | Yes | Algorithm 1: FTRL Input : Time horizon T, step size η, α-strongly-convex regularizer R : X R. ... Algorithm 2: Mini-Batch FTRL Input : Time horizon T, step size η, α-strongly-convex regularizer R : X R, batch size S. |
| Open Source Code | Yes | https://github.com/raunakkmr/oco-with-memory-code. |
| Open Datasets | No | We sample the disturbances {wt} from a standard normal distribution. |
| Dataset Splits | No | The paper describes simulation experiments where disturbances are sampled from a standard normal distribution. It does not mention traditional training, validation, or test dataset splits in the context of these simulations. |
| Hardware Specification | No | We run the experiments on a standard laptop. |
| Software Dependencies | No | We use the cvxpy library [Diamond and Boyd, 2016, Agrawal et al., 2018] for implementing Algorithm 1. The paper mentions the name of the library but does not specify its version number, nor does it list any other software dependencies with version numbers. |
| Experiment Setup | Yes | We set the time horizon T = 750 and dimension d = 2. We sample the disturbances {wt} from a standard normal distribution. We set the system matrix G to be the identity and the system matrix F to be a diagonal plus upper triangular matrix with the diagonal entries equal to ρ and the upper triangular entries equal to α. We run simulations with various values of ρ and α. ... We use step-sizes according to Theorems 3.1 and 3.3. |