Online Learning with Local Permutations and Delayed Feedback
Authors: Ohad Shamir, Liran Szlak
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide some experiments validating the performance of our algorithm. |
| Researcher Affiliation | Academia | 1Weizmann Institute of Science, Rehovot, Israel. Correspondence to: Liran Szlak <liran.szlak@weizmann.ac.il>. |
| Pseudocode | Yes | Algorithm 1 Delayed Permuted Mirror Descent |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses a custom-generated adversarial setting based on linear loss functions, described in Section 4, rather than a publicly available dataset with specified access information. |
| Dataset Splits | No | The paper describes a simulated adversarial setting for experiments, which does not involve standard training, validation, and test dataset splits common in empirical studies with fixed datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency names with version numbers that would be necessary for replication. |
| Experiment Setup | Yes | In all experiments we use T = 105 rounds, a delay parameter of τ = 200, set our step sizes according to the theoretical analysis, and report the mean regret value over 1000 repetitions of the experiments. |