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..
Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization
Authors: Yueming LYU
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on challenging benchmark test functions and black-box prompt fine-tuning for large language models demonstrate the query efficiency of our RLTS technique. We first evaluate our RLTS on challenging benchmark test functions: Rosenbrock, Rastrigin, and Nesterov. |
| Researcher Affiliation | Academia | Yueming LYU Centre for Frontier AI Research (CFAR) Institute of High Performance Computing (IHPC) Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632 EMAIL |
| Pseudocode | Yes | Algorithm 1 Fast Coordinate Search and Algorithm 2 Rank-1 Lattice Targeted Sampling |
| Open Source Code | No | The paper mentions using "publicly available code3" for a backbone model (Sun et al., 2022a), but there is no explicit statement or link indicating that the authors' own RLTS implementation is open-source. |
| Open Datasets | Yes | Six benchmark datasets for different language tasks are employed for evaluation: DBpedia, SS2, SNLI, AG s News, MRPC and RTE. The SST2 [Socher et al., 2013] dataset is a dataset for the sentiment analysis task. AG s News and DBPedia datasets [Zhang et al., 2015] are used for topic classification tasks. SNLI [Bowman et al., 2015] and RTE [Wang et al., 2019] are employed for natural language inference. MRPC dataset [Dolan and Brockett, 2005] is used for the paraphrasing task. |
| Dataset Splits | No | The paper specifies batch sizes and number of independent runs for experiments but does not explicitly provide details about training/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | All the experiments are performed in 50 runs on a single NVIDIA A40 Card. |
| Software Dependencies | No | The paper mentions "Pytorch" as a deep learning toolbox and "cma" package, but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For all the methods, we initialize the ยต = 0. For INGO and RLTS, we set the step-size parameter ฮฒ = 0.2 in all experiments. For RLTS, we set the parameter ฮท = 1 in all experiments. We initialized ฮฃ = I for all the methods. The number of epochs of training is set to 2000. The number of iterations of fast coordinate search is set to T = 50. |