Best Response Regression
Authors: Omer Ben-Porat, Moshe Tennenholtz
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also test our approach in a high-dimensional setting, and show it significantly defeats classical regression algorithms in the prediction duel. The theoretical analysis is complemented by an experimental study, which illustrates the effectiveness of our approach. |
| Researcher Affiliation | Academia | Omer Ben-Porat Technion Israel Institute of Technology Haifa 32000 Israel omerbp@campus.technion.ac.il Moshe Tennenholtz Technion Israel Institute of Technology Haifa 32000 Israel moshet@ie.technion.ac.il |
| Pseudocode | Yes | Algorithm: EMPIRICAL PAYOFF MAXIMIZATION (EPM) |
| Open Source Code | Yes | Code for reproducing the experiments is available at https://github.com/omerbp/Best-Response-Regression |
| Open Datasets | Yes | The MILP algorithm is tested on the Boston housing dataset [5]. |
| Dataset Splits | Yes | The dataset was split into training (80%) and test (20%) sets, and two scenarios were considered: |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | We employed the MILP formulation, and used Gurobi software [4] in order to find a response, where the running time of the solver was limited to one minute. The paper mentions Gurobi software but does not specify its version number. |
| Experiment Setup | Yes | We employed the MILP formulation, and used Gurobi software [4] in order to find a response, where the running time of the solver was limited to one minute. |