Online Learning with Feedback Graphs Without the Graphs
Authors: Alon Cohen, Tamir Hazan, Tomer Koren
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph... We prove that even for dense feedback graphs, the learner cannot improve upon a trivial regret bound... we give an algorithm that achieves eΘ(αT) regret... We also extend our results to a more general feedback model... For our algorithm in the stochastic case, we also prove a distribution-dependent regret bound... |
| Researcher Affiliation | Academia | Alon Cohen ALON.COHEN@TECHNION.AC.IL Tamir Hazan TAMIR.HAZAN@TECHNION.AC.IL Tomer Koren TOMERK@TECHNION.AC.IL Technion Israel Institute of Technology, Haifa, Israel |
| Pseudocode | Yes | Algorithm 1 input Set V of K actions, number of rounds T initialize r 1, V1 = V while |Vr| > 1 and T rounds have not elapsed do... Algorithm 2 ALPHASAMPLE input Set of actions U V initialize S while |U| > 0 do... |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is being released. |
| Open Datasets | No | The paper is theoretical, presenting algorithms and proving bounds; it does not involve experimental evaluation on datasets. Therefore, no information about training datasets or their public availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments with dataset splits. Thus, there is no mention of training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs; it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Thus, there are no details provided regarding experimental setup, hyperparameters, or training configurations. |