Algorithms for Average Regret Minimization
Authors: Sabine Storandt,Stefan Funke1600-1607
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are accompanied with experiments on a variety of inputs with d up to 7. Experiments We implemented the proposed algorithms for average regret in C++, using CGAL 4.11 for computing convex hulls and Eigen 3.3.4 for determining volumes. Experiments were conducted on an AMD Ryzen 2400G with 3.6GHz and 64GB RAM. For benchmarking, we use a variety of different inputs with the following characteristics: RC Random points in the unit hypercube (synthetic). We generated up to n = 10^6 points for d from 3 to 6. El Nino Oceanographic data (real-world). It has n = 178080 points for d = 5 (zonal and meridional wind speed, water and surface temperature, relative humidity). Air Data Flight statistics (real-world). It has n = 458311 points with d = 7 (distance, air-time, arr-dely, dep-delay, taxi-out, taxi-in, actual-elapsed-time). Weather Mean temperatures for every January and July (d = 2) for n = 566262 locations around the world. |
| Researcher Affiliation | Academia | Sabine Storandt,1 Stefan Funke2 1University of Konstanz, Germany, 2University of Stuttgart, Germany |
| Pseudocode | No | The paper describes algorithms such as the greedy algorithm and reverse greedy algorithm in text, but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper states 'We implemented the proposed algorithms for average regret in C++', but does not provide any specific link or explicit statement about the public availability of the source code for the methodology described. |
| Open Datasets | Yes | El Nino Oceanographic data (real-world). It has n = 178080 points for d = 5... Air Data Flight statistics (real-world). It has n = 458311 points with d = 7... Weather Mean temperatures for every January and July (d = 2) for n = 566262 locations around the world. The real-world data sets were all used before in related publications, e.g., (Soma and Yoshida 2017; Kumar and Sintos 2018). |
| Dataset Splits | No | The paper does not specify dataset splits for training, validation, and testing, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | Experiments were conducted on an AMD Ryzen 2400G with 3.6GHz and 64GB RAM. |
| Software Dependencies | Yes | We implemented the proposed algorithms for average regret in C++, using CGAL 4.11 for computing convex hulls and Eigen 3.3.4 for determining volumes. |
| Experiment Setup | Yes | To approximate the volume below the upper envelope, we simply multiply for each α P the volume of a (d 1)-dimensional cube of side length ϵ with maxs S αT s. The running time for such an approximate volume approximation is then O(nϵ (d 1)). Clearly, the smaller ϵ, the better the approximation. We report running times and regret values for the exact variant as well as for the heuristic version, where we estimate the volume of S + s for all s S \ S in each round based on sampling. To judge the quality of the heuristic greedy truthfully, we compute the exact happiness volume induced by the final subset S . For the RC benchmark, the results are summarized in Table 1. For small q, the average regret values for the exact and heuristic version are the same in almost all settings. |