Starting Small - Learning with Adaptive Sample Sizes
Authors: Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann
ICML 2016 | Conference PDF | Archive PDF | Plain Text | 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. |