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..
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]. |