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].
Minimizing Quadratic Functions in Constant Time
Authors: Kohei Hayashi, Yuichi Yoshida
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical performance (accuracy and runtime) is positively confirmed by numerical experiments. In this section, we demonstrate the effectiveness of our method by experiment. |
| Researcher Affiliation | Collaboration | Kohei Hayashi National Institute of Advanced Industrial Science and Technology EMAIL Yuichi Yoshida National Institute of Informatics and Preferred Infrastructure, Inc. EMAIL |
| Pseudocode | Yes | Algorithm 1 Input: An integer n N, query accesses to the matrix A Rn n and to the vectors d, b Rn, and ϵ, δ > 0 1: S a sequence of k = k(ϵ, δ) indices independently and uniformly sampled from {1, 2, . . . , n}. 2: return n2 k2 minv Rn pk,A|S,d|S,b|S(v). |
| Open Source Code | Yes | 1The program codes are available at https://github.com/hayasick/CTOQ. |
| Open Datasets | No | We randomly generated the data sets as xi N(1, 0.5) for i [n] and x j N(1.5, 0.5) for j [n ] where N(µ, σ2) denotes the Gaussian distribution with mean µ and variance σ2. (No specific public dataset, link, or citation for the dataset itself provided.) |
| Dataset Splits | Yes | σ2 and λ were chosen by 5-fold cross-validation as suggested in [21]. |
| Hardware Specification | Yes | All experiments were conducted on an Amazon EC2 c3.8xlarge instance. |
| Software Dependencies | No | 2We used GLPK (https://www.gnu.org/software/glpk/) for the QP solver. (No version number for GLPK provided.) |
| Experiment Setup | Yes | In this experiment, we used the Gaussian kernel φ(x, y) = exp((x y)2/2σ2) and set n = 200 and α = 0.5; σ2 and λ were chosen by 5-fold cross-validation as suggested in [21]. We randomly generated inputs as Aij U[ 1,1], di U[0,1], and bi U[ 1,1] for i, j [n], where U[a,b] denotes the uniform distribution with the support [a, b]. |