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 Robustness of Estimator Composition
Authors: Pingfan Tang, Jeff M. Phillips
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this simulation we actually construct a method to relocate an estimator by modifying the smallest number of points possible... To show a simulation of this process, we use a uniform distribution to randomly generate nk points... Table 1 shows the result of running this experiment for different n and k... |
| Researcher Affiliation | Academia | Pingfan Tang School of Computing University of Utah Salt Lake City, UT 84112 EMAIL Jeff M. Phillips School of Computing University of Utah Salt Lake City, UT 84112 EMAIL |
| Pseudocode | Yes | The algorithm framework is then as follows, using the above gradient descent formulation at each step. We ο¬rst compute the L1-median mi for each Pi, and then change n points in {m1, m2, , mn} to obtain {m 1, m 2, , m n, m n+1, , mn} such that median(m 1, m 2, , m n, m n+1, , mn) = p0. For each m i, we change k points in Pi to obtain e Pi = {p i,1, p i,2, , p i, k, pi, k+1, , pi,k} such that median( e Pi) = m i. |
| Open Source Code | No | The paper does not provide an unambiguous statement or a direct link to a code repository for the work described. |
| Open Datasets | No | The simulation uses data generated from a uniform distribution ('we use a uniform distribution to randomly generate nk points') rather than a specific publicly available or open dataset with concrete access information. |
| Dataset Splits | No | The paper describes a simulation with generated data but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the simulations or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) used to replicate the experiments. |
| Experiment Setup | Yes | To show a simulation of this process, we use a uniform distribution to randomly generate nk points in the region [ 10, 10] [ 10, 10], and generate a target point p0 = (x0, y0) in the region [ 20, 20] [ 20, 20]... and then update x and y along the negative gradient direction of h, until the Euclidean norm of gradient is less than 0.00001. |