On the ERM Principle With Networked Data
Authors: Yuanhong Wang, Yuyi Wang, Xingwu Liu, Juhua Pu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | in this paper we mainly prove new universal risk bounds for CLANET whose goal is to train a classifier with examples in a data graph G. formulate a non-convex optimization problem inspired by our new risk bounds and then we also design a new efficient algorithm to obtain an approximate optimal weighting vector and show that this algorithm is a fully polynomial-time approximation scheme for this non-convex program. |
| Researcher Affiliation | Academia | 1 State Key Laboratory of Software Development Environment, Beihang University, Beijing, China 2 Research Institute of Beihang University in Shenzhen, Shenzhen, China 3 Disco Group, ETH Zurich, Switzerland 4 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 5 University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1 FPTAS for weighting vector optimization. |
| Open Source Code | No | The paper provides a link to an online appendix (https://arxiv.org/abs/1711.04297) for supplementary details, but does not explicitly state that source code for their method is released or available. |
| Open Datasets | No | The paper is theoretical and does not mention or provide access to any specific datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training settings. |