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 Composite Optimization Between Stochastic and Adversarial Environments
Authors: Yibo Wang, SIJIA CHEN, Wei Jiang, Wenhao Yang, Yuanyu Wan, Lijun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we also conduct empirical studies in Appendix A to verify our theoretical results. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2School of Artificial Intelligence, Nanjing University, Nanjing, China 3School of Software Technology, Zhejiang University, Ningbo, China |
| Pseudocode | Yes | Algorithm 1 Optimistic Composite Mirror Descent (Opt CMD) |
| Open Source Code | No | While the code is not included in the submission, the complete detailed descriptions of the experiments are provided in Section A. |
| Open Datasets | Yes | To verify our theoretical findings, we conduct experiments on the mushroom datasets from the LIBSVM repository [Chang and Lin, 2011] |
| Dataset Splits | No | The paper mentions sampled data for each round but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments in the main text or Appendix A. |
| Software Dependencies | No | The paper mentions using 'LIBSVM' and several algorithmic frameworks (OGD, COMID, Optimistic-OMD, ONS, Prox ONS) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Let T denote the number of the total rounds. At each round t [T], the learner receives a sampled data (xt, yt) Rd { 1, 1} with d = 112. Then, the learner plays the decision wt from the ball X with the diameter D = 20, and suffers a composite loss φt(wt; xt, yt) = ft(wt; xt, yt) + λr(wt), where we set the hyper-parameter λ = 0.001. ... All parameters of each method are set according to their theoretical suggestions. For instance, in the general convex case, the learning rate is set as η = ct 1/2 in OGD, and η = c T 1/2 in COMID, ηt = D(c + Vt 1) 1/2 in Optimistic-OMD where c denotes the hyper-parameter selected from {10 3, 10 2, , 104}. |