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..
Contextual Covariance Matrix Adaptation Evolutionary Strategies
Authors: Abbas Abdolmaleki, Bob Price, Nuno Lau, Luis Paulo Reis, Gerhard Neumann
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For illustration of the algorithm, we will use contextual standard functions and two contextual simulated robotic tasks which are robot table tennis, and a robot kick task. We show that our contextual CMA-ES algorithm performs favourably in comparison to other contextual learning algorithms. |
| Researcher Affiliation | Collaboration | Abbas Abdolmaleki1,2,3, Bob Price5, Nuno Lau1, Luis Paulo Reis2,3, Gerhard Neumann4 1: IEETA, DETI, University of Aveiro 2: DSI, University of Minho 3: LIACC, University of Porto, Portugal 4: CLAS, TU Darmstadt, Germany 5: PARC, A Xerox Company, USA |
| Pseudocode | Yes | Algorithm 1 Contextual Stochastic Search Algorithms |
| Open Source Code | Yes | Matlab code for reproducing the results on standard functions as well as videos regarding the experiments (table tennis and robot kick) at https://goo.gl/MLz Ks W |
| Open Datasets | Yes | We chose two series of optimization tasks for comparisons. In the ο¬rst series, we use the standard optimization test functions [Molga and Smutnicki, 2005] |
| Dataset Splits | No | No explicit training/validation/test dataset splits (percentages, counts, or references to predefined splits) were found. The paper describes an iterative optimization process based on generated samples rather than fixed data partitions. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments were mentioned. |
| Software Dependencies | No | For example we use fmincon tool in matlab. |
| Experiment Setup | Yes | For all experiments, the KL-bound Ο΅ for REPS is set to 1 and for all experiments we use the default hyper parameter settings given in Algorithm 1 without further tuning. |