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..
Risk-Averse No-Regret Learning in Online Convex Games
Authors: Zifan Wang, Yi Shen, Michael Zavlanos
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our method on an online market problem that we model as a Cournot game (Allaz & Vila, 1993). ... In section 5, we use an online market example to illustrate the effectiveness of the proposed algorithms. ... 5. Numerical Experiments |
| Researcher Affiliation | Academia | 1School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden. 2Department of Mechanical Engineering & Material Science, Duke University, Durham, NC 27708, USA. |
| Pseudocode | Yes | Algorithm 1 Risk-averse learning ... Algorithm 2 Risk-averse learning with sample reuse ... Algorithm 3 Risk-averse learning with residual feedback |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a synthetic Cournot game setup with a defined cost function and a uniform random variable for uncertainty, rather than using a publicly available or open dataset. For example: "The cost term ξixi models the uncertainty in the market, which is proportional to production." |
| Dataset Splits | No | The paper describes a numerical simulation setup for a Cournot game and does not specify training, validation, or test dataset splits, as it's not based on pre-existing data splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | In Algorithm 1, 2, and 3, the requirements are listed as: "Initial value x0, step size η, parameters a, b, δ, T, risk level αi, i = 1, , N.". In Section 5, "Numerical Experiments", it states: "We let α0 = 0.5 and α1 = 0.3, i.e, firm 1 is more risk sensitive than firm 0." and "We implement a hybrid sampling strategy for Algorithm 2 and select the switching time step as 15000". It also mentions: "All other parameters in Algorithms 1, 2 and 3 are tuned so that the three algorithms achieve individually their best performance." |