The power of absolute discounting: all-dimensional distribution estimation
Authors: Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the theory via synthetic data and an application to the Global Terrorism Database. Lastly, we give some synthetic experiments in Section 8 and then explore forecasting global terror incidents on real data [LDMN16] |
| Researcher Affiliation | Academia | Moein Falahatgar UCSD moein@ucsd.edu Mesrob Ohannessian TTIC mesrob@gmail.com Alon Orlitsky UCSD alon@ucsd.edu Venkatadheeraj Pichapati UCSD dheerajpv7@ucsd.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. The paper describes the absolute discounting estimator with a formula but not as an algorithm. |
| Open Source Code | No | No explicit statement or link regarding the release or availability of the source code for the described methodology was found. |
| Open Datasets | Yes | Lastly, we give some synthetic experiments in Section 8 and then explore forecasting global terror incidents on real data [LDMN16]. [LDMN16] Gary La Free, Laura Dugan, Erin Miller, and National Consortium for the Study of Terrorism and Responses to Terrorism. Global Terrorism Database, 2016. |
| Dataset Splits | Yes | The forecasting problem that we solve is to estimate the number of total incidents in a subset of the cities over the coming year, using the current year s data from all cities. To take advantage of the abundance of data, in this case at each time point we used the previous 2, 000 incidents for learning, and predicted the share of each city for the next 2, 000 incidents. |
| Hardware Specification | No | No specific hardware details (such as GPU models, CPU types, or memory specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details, such as library names with version numbers, were provided in the paper. |
| Experiment Setup | Yes | In all synthetic experiments, we use 500 Monte Carlo iterations. Also, we set the discount value based on data, δ = min{ max(Φ1,1) D , 0.9}. |