Learning Time-Varying Coverage Functions
Authors: Nan Du, Yingyu Liang, Maria-Florina F Balcan, Le Song
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. |
| Researcher Affiliation | Academia | Nan Du , Yingyu Liang , Maria-Florina Balcan , Le Song College of Computing, Georgia Institute of Technology Department of Computer Science, Princeton University School of Computer Science, Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1 TCOVERAGELEARNER |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Meme Tracker is a real-world dataset [18] to study information diffusion. |
| Dataset Splits | Yes | We use 8,192 random features and two-fold cross validation on the train data to tune the normalization Z... Each set of cascades is split into 60%-train and 40%-test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for dependencies. |
| Experiment Setup | Yes | We use 8,192 random features and two-fold cross validation on the train data to tune the normalization Z, which has the best value 1130, 1160, 1020, and 1090, respectively. We choose the RBF kernel bandwidth h = 1/2π so that the magnitude of the smoothed approximate function still equals to 1 (or it can be tuned by cross-validation as well), which matches the original indicator function. |