Strongly Adaptive Online Learning
Authors: Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms to strongly adaptive. As a consequence, we derive simple, yet efficient, strongly adaptive algorithms for a handful of problems.In this section we sketch the proof of Theorem 1. A full proof is detailed in Appendix 1. The analysis of SAOL is divided into two parts. The first challenge is to prove the theorem for the intervals in I (see Lemma 2). Then, the theorem should be extended to any interval (end of Appendix 1). |
| Researcher Affiliation | Academia | Amit Daniely AMIT.DANIELY@MAIL.HUJI.AC.IL Alon Gonen ALONGNN@CS.HUJI.AC.IL Shai Shalev-Shwartz SHAIS@CS.HUJI.AC.IL The Hebrew University |
| Pseudocode | Yes | Algorithm 1 Strongly Adaptive Online Learner (with blackbox algorithm B) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper focuses on theoretical contributions and does not describe experiments using specific datasets, thus no information about publicly available datasets used for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with dataset splits, so no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis, therefore it does not describe an experimental setup with specific hyperparameters or training configurations. |