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..
The Common-directions Method for Regularized Empirical Risk Minimization
Authors: Po-Wei Wang, Ching-pei Lee, Chih-Jen Lin
JMLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method outperforms state-of-the-art firstand second-order optimization methods in terms of the number of data accesses, while is competitive in training time. |
| Researcher Affiliation | Academia | Po-Wei Wang EMAIL Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213, USA Ching-pei Lee EMAIL Department of Computer Sciences University of Wisconsin Madison Madison, WI 53706-1613, USA Chih-Jen Lin EMAIL Department of Computer Science National Taiwan University Taipei 106, Taiwan |
| Pseudocode | Yes | Algorithm 1: A framework for the common-directions method Algorithm 2: Backtracking line search Algorithm 3: The common-directions method for solving the ERM problem (7) Algorithm 4: Common-directions method with multiple inner iterations Algorithm 5: Common-directions method with multiple inner iterations for the ERM problem (7) |
| Open Source Code | Yes | Programs used for experiments and a supplementary file including additional results can be found at http://www.csie.ntu.edu.tw/~cjlin/papers/commdir/. |
| Open Datasets | Yes | We consider the data sets listed in Table 1 with three different choices of parameters C = {10 3, 1, 103} to examine the situation of different condition numbers. All data sets except yahoo-japan and yahoo-korea are publicly available.8 [...] Footnote 8: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets. |
| Dataset Splits | No | The paper lists data sets used in Table 1 with 'Training size (l)', 'Features (n)', and 'Density (#nnz/(ln))', but it does not specify any training/test/validation splits or their percentages/methodology. |
| Hardware Specification | Yes | All experiments are conducted on a 64-bit machine with Intel Xeon 2.0 GHz CPU (E5504), 4MB cache, and 32GB memory. |
| Software Dependencies | No | All algorithms are implemented in C++. This specifies a programming language but does not provide specific version numbers for any libraries, frameworks, or solvers used in their own implementation. |
| Experiment Setup | Yes | For the backtracking line search procedure in Algorithm 2, we set β = 0.4, λ = 0.25. The solution to the linear system (14) is obtained by a Cholesky factorization with partial pivoting. For the multiple inner iteration variant, we use the following heuristic stopping condition for the inner loop in Algorithm 4. Pk f(w) min(0.1 f(wk) , f(wk) 2). [...] L-BFGS: We implement the L-BFGS algorithm by considering different numbers of history states m = 5, 10, 15, 20, 30. [...] For NEWTON, [...] CG generates an approximate solution p of (35) that satisfies f(w) 2f(w)p 0.1 f(w) . [...] Nesterov s accelerated gradient (AG): we consider the approach proposed by Nesterov (2013) that adaptively estimates the parameters σ and ρ by a procedure similar to backtracking line search. We take the initial estimation to be σ = 1 and ρ = 1/Cl. |