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..
Starting Small - Learning with Adaptive Sample Sizes
Authors: Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experimental Results We present experimental results on synthetic as well as real-world data, which largely confirms the above analysis. |
| Researcher Affiliation | Academia | Department of Computer Science, ETH Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 2 DYNASAGA |
| Open Source Code | No | No concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Table 2. Details of the real datasets used in our experiments. All datasets were selected from the LIBSVM dataset collection. |
| Dataset Splits | Yes | The training set includes 90% of the data. The vertical axis shows the suboptimality of the expected risk, i.e. log2 E10 RS(wt) RS(w T ) , where S is a test set which includes 10% of the data and w T is the optimum of the empirical risk on T . |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments are provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. |
| Experiment Setup | Yes | Throughout all the experiments we used the logistic loss with a regularizer λ = 1 n 3. The various parameters used for the baseline methods are described in Table 3. A critical factor in the performance of most baselines, especially SGD, is the selection of the step-size. We picked the best-performing step-size within the common range guided by existing theoretical analyses, specifically η = 1/L and η = C C+µt for various values of C. |