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 [1].
Fast Nonsmooth Regularized Risk Minimization with Continuation
Authors: Shuai Zheng, Ruiliang Zhang, James T. Kwok
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on nonsmooth classification and regression tasks demonstrate that the proposed algorithm outperforms the state-of-the-art. |
| Researcher Affiliation | Academia | Shuai Zheng, Ruiliang Zhang, James T. Kwok Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1 CNS algorithm for strongly convex problems. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We only report results on two data sets (Table 3) from the LIBSVM archive: (i) the popularly used classification data set rcv1; and (ii) Year Prediction MSD, the largest regression data in the LIBSVM archive, and is a subset of the Million Song data set. |
| Dataset Splits | Yes | ν1, ν2 are tuned by 5-fold cross-validation. For each method, the stepsize is tuned by running on a subset containing 20% training data for a few epochs (for the proposed method, we tune η1). |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU types, or cloud computing resources used for the experiments. |
| Software Dependencies | No | All algorithms are implemented in Matlab. The paper does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | The mini-batch size b is 50 for rcv1, and 100 for Year Prediction MSD. We set γ1 = 0.01, τ = 2, and T1 = n/b. For each method, the stepsize is tuned by running on a subset containing 20% training data for a few epochs (for the proposed method, we tune η1). We set λ1 in Algorithm 2 to 10 5 for rcv1, and 10 7 for Year Prediction MSD. |