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.
Constrained Phi-Equilibria
Authors: Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco TrovΓ², Nicola Gatti
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 28,419 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,974 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce and computationally characterize constrained Phi-equilibria a more general notion than constrained CEs in normal-form games. We show that computing such equilibria is in general computationally intractable, and also that the set of the equilibria may not be convex, providing a sharp divide with unconstrained CEs. Nevertheless, we provide a polynomial-time algorithm for computing a constrained (approximate) Phi-equilibrium maximizing a given linear function, when either the number of constraints or that of players actions is fixed. |
| Researcher Affiliation | Academia | Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy. |
| Pseudocode | Yes | Algorithm 1 Learning a Constrained Ο΅-Phi-equilibria |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository. This is a theoretical paper that focuses on algorithms and complexity analysis rather than practical implementation. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies that would involve datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies that would involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity and algorithm design. It does not describe any experimental setup or implementation details that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and describes algorithms (e.g., ellipsoid algorithm, online gradient descent) and their complexity, but it does not detail any specific software implementations or list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include empirical experiments. Therefore, there are no experimental setup details, hyperparameters, or training configurations described. |