The Robustness of Estimator Composition
Authors: Pingfan Tang, Jeff M. Phillips
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 tang1984@cs.utah.edu Jeff M. Phillips School of Computing University of Utah Salt Lake City, UT 84112 jeffp@cs.utah.edu |
| Pseudocode | Yes | The algorithm framework is then as follows, using the above gradient descent formulation at each step. We first 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. |