Variational Policy for Guiding Point Processes
Authors: Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world data show that our algorithm can steer the user activities much more accurately and efficiently than other stochastic control methods. |
| Researcher Affiliation | Academia | 1College of Computing, Georgia Tech, Atlanta, GA, USA 2School of Aerospace Engineering, Georgia Tech. Correspondence to: Yichen Wang <yichen.wang@gatech.edu>. |
| Pseudocode | Yes | Algorithm 1 KL Model Predictive Control |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that the code will be made open source. |
| Open Datasets | Yes | We evaluate on a real-world Twitter dataset (Farajtabar et al., 2015) |
| Dataset Splits | No | We partition the data into ten intervals and use one interval for training and others for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU or GPU models used for running experiments. |
| Software Dependencies | No | The paper mentions methods like 'Euler forward method' and 'thinning algorithm' but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For MPC, we set the optimization window T = T/10 and sample size I = 10, 000. ... The time window is divided into 500 timestamps. We set the initial opinion xi(0) = 10 and the target opinion ai = 1 for each user. For model parameters, we set β = 0.2, and adjacency matrix A generated uniformly on [0, 0.01] with sparsity of 0.001. We set the base intensity in (1) to be µ = 0.01; the influence matrix is the same as the adjacency matrix A. We set the cost tradeoff parameter to be γ = 10. |