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..
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
Authors: Rodolphe Jenatton, Jim Huang, Cedric Archambeau
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We supplement the analysis with experiments validating the performance of our algorithm in practice.We ran two sets of experiments to assess the performance for our adaptive algorithms for OCO with long-term constraints and compare to the algorithms proposed by Mahdavi et al. (2012a). |
| Researcher Affiliation | Industry | Rodolphe Jenatton , Jim C. Huang , Cedric Archambeau EMAIL Amazon, Berlin, Germany, Seattle, USA |
| Pseudocode | Yes | Initialize x1 = 0 and λ1 = 0. For t {1, , T 1}: xt+1 = ΠB(xt ηt x Lt(xt, λt)), λt+1 = ΠR+(λt + µt λLt(xt, λt)), |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We solve the above problem using the datasets ijcnn1 and covtype, consisting respectively of 49, 990 and 581, 012 samples of dimension d = 22 and d = 54 each.2 www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/binary.html. |
| Dataset Splits | No | The paper describes using datasets 'ijcnn1' and 'covtype' and discusses generating sequences, but it does not specify explicit train/validation/test splits, percentages, or sample counts for these datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'CVXPY' and an implementation based on 'Defazio et al. (2014)' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The parameter ρ is set to obtain approximately 30% of nonzero variables. They are computed over T = 1000 iterations with d = 64, and are averaged over 10 random sequences {Yt}T t=1. |