Robust Budget Pacing with a Single Sample
Authors: Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is to show that Dual Follow-The-Regularized-Leader (FTRL) is robust and achieves the near optimal O( T)-regret even when it has access to just one sample from each distribution Pt, dramatically improving over the prior T log T samples from each distribution (Jiang et al., 2020) to achieve O( T)-regret, while still being robust to noise in the sampling distributions. |
| Researcher Affiliation | Collaboration | 1DRO, Columbia Business School, New York, NY, USA 2Google Research, New York, NY, USA 3IEOR, Columbia University, New York, NY, USA. Work done as a Student Researcher at Google Research. |
| Pseudocode | Yes | Algorithm 1 Learning the Dual and Earning with It |
| Open Source Code | No | The paper does not provide any specific link or explicit statement about the availability of source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments or specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |