Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Learning with Feedback Graphs Without the Graphs
Authors: Alon Cohen, Tamir Hazan, Tomer Koren
ICML 2016 | Venue PDF | 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 EMAIL Tamir Hazan EMAIL Tomer Koren EMAIL 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. |