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 [1].
On the optimality of the Hedge algorithm in the stochastic regime
Authors: Jaouad Mourtada, Stéphane Gaïffas
JMLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate our theoretical results by numerical experiments that compare the behavior of various Hedge algorithms in the stochastic regime. We report in Figure 1 the cumulative regrets of the considered algorithms in four examples. |
| Researcher Affiliation | Academia | Jaouad Mourtada EMAIL Centre de Mathematiques Appliquees Ecole Polytechnique Palaiseau, FranceStephane Gaiffas EMAIL Laboratoire de Probabilites, Statistique et Modelisation Universite Paris Diderot Paris, France |
| Pseudocode | No | The paper describes algorithms such as Hedge, Decreasing Hedge, Constant Hedge, and Hedge with doubling trick using mathematical formulations and prose, but it does not contain a dedicated pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code, nor does it include links to a code repository in the main text or supplementary materials. While it references "Koolen (2018)" which includes a blog link, this refers to another author's work and not the current paper's implementation. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments: "(a) Stochastic instance with a gap. This is the standard instance considered in this paper. The losses are drawn independently from Bernoulli distributions (one of parameter 0.3, 2 of parameter 0.4 and 7 of parameter 0.5, so that M = 10 and = 0.1)." It does not refer to any pre-existing public datasets with access information. |
| Dataset Splits | No | The paper describes generating synthetic stochastic instances with specific parameters for its numerical experiments (e.g., "losses are drawn independently from Bernoulli distributions"). It does not involve partitioning a pre-existing dataset into training, validation, or test splits, as is common with empirical studies using fixed datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the numerical experiments or simulations. |
| Software Dependencies | No | The paper describes the algorithms and their parameters for the numerical illustrations but does not specify any software dependencies or versions (e.g., programming languages, libraries, frameworks) used for implementation. |
| Experiment Setup | Yes | We consider the following algorithms: hedge is Decreasing Hedge with the default learning rates ηt = 2 p log(M)/t, hedge constant is Constant Hedge with constant learning rate ηt = p 8 log(M)/T, hedge doubling is Hedge with doubling trick with c0 = 8, adahedge is the Ada Hedge algorithm from de Rooij et al. (2014), which is a variant of the Hedge algorithm with a data-dependent tuning of the learning rate ηt (based on ℓ1, . . . , ℓt 1). |