Online Learning under Adversarial Nonlinear Constraints
Authors: Pavel Kolev, Georg Martius, Michael Muehlebach
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also empirically evaluate our algorithm on two-player games, where the players are subjected to a shared constraint. We apply our algorithm and show numerical experiments that support our theoretical findings. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2University of Tübingen, Tübingen, Germany |
| Pseudocode | Yes | Algorithm 1 Constraint Violation Velocity Projection (CVV-Pro) |
| Open Source Code | No | The paper states 'For the implementation of CVV-Pro we have used the MATLAB R2019a numerical computing software.' but does not provide a link or explicit statement about the release of their source code. |
| Open Datasets | No | The paper describes generating random instances for the two-player game simulation, stating 'Each component of the utility matrix A Rn n is sampled from the normal distribution and the constraint matrices Cx, Cy [0, 1]m n have each of their components sampled uniformly at random from [0, 1]'. No publicly available dataset is used or linked. |
| Dataset Splits | No | The paper describes numerical simulations with generated data but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | The computation of the experiment takes about 4 hours on a machine with CPU: Intel(R) i7-6800K 3.40 GHz with 6 cores, GPU: NVIDIA Ge Force GTX 1080, and RAM: 32 GB. |
| Software Dependencies | Yes | For the implementation of CVV-Pro we have used the MATLAB R2019a numerical computing software. |
| Experiment Setup | Yes | We implemented our algorithm with ηt = 1/(αt) and α = 100. We report results from numerical simulations with decision dimension n = 100, m = 10 shared resource constraints, T = 4000 iterations, and five independently sampled instances of the two-player game. |