Online Convex Optimization with Unbounded Memory
Authors: Raunak Kumar, Sarah Dean, Robert Kleinberg
NeurIPS 2023 | Conference 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 raunak@cs.cornell.edu Sarah Dean Department of Computer Science Cornell University Ithaca, NY 14853 sdean@cornell.edu Robert Kleinberg Department of Computer Science Cornell University Ithaca, NY 14853 rdk@cs.cornell.edu |
| 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. |