Estimation of Spectral Risk Measures
Authors: Ajay Kumar Pandey, Prashanth L.A., Sanjay P. Bhat12166-12173
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the theoretical findings on a synthetic setup, and in a vehicular traffic routing application. ... Third, we perform simulation experiments to show the efficacy of our proposed SRM estimation scheme. In particular, we consider a synthetic setup, and show that our scheme provides accurate estimates of SRM. ... We test the resulting SR algorithm variant in a vehicular traffic routing application using SUMO traffic simulator (Behrisch et al. 2011). |
| Researcher Affiliation | Collaboration | Ajay Kumar Pandey, 1 Prashanth L. A., 1 Sanjay P. Bhat 2 1 Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India 2 TCS Research, Hyderabad, India, and Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We use the street map of the area around IIT Madras, Chennai, India (see Figure 2) obtained from Open Street Map (OSM) (Haklay and Weber 2008), and then used Netconvert tool to load the map in SUMO. |
| Dataset Splits | No | The paper describes generating samples for synthetic setup and using cars as samples for the traffic routing application but does not specify train/validation/test dataset splits, as it is not a supervised learning task. |
| 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 mentions using 'SUMO' and 'Tra CI' for implementation but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In our experiments, we use the risk aversion function ϕ(β) = 5 e 5(1 β) / (1 e 5), β [0, 1]. ... The samples are generated using a Gaussian distribution with mean 0.5 and variance 25. ... The network has 426 junctions and a total edge length of 123 km. We ran SUMO on this network for 30, 000 time-steps, in which 7000 cars, 500 buses, 2000 bikes, 1000 cycles, and 1000 pedestrians were added at different time-steps and in different lanes uniformly. We choose K = 5 routes between two fixed points... On these selected routes, we added n = 1000 cars and tracked them. ... We set the number of subdivisions m = 100. |