Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Online Learning under Adversarial Nonlinear Constraints
Authors: Pavel Kolev, Georg Martius, Michael Muehlebach
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,745 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,605 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |