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.
Conditional Swap Regret and Conditional Correlated Equilibrium
Authors: Mehryar Mohri, Scott Yang
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 13,473 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,743 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 | We prove a series of new results for conditional swap regret minimization. We present algorithms for minimizing conditional swap regret with bounded conditioning history. We further extend these results to the case where conditional swaps are considered only for a subset of actions. We also define a new notion of equilibrium, conditional correlated equilibrium, that is tightly connected to the notion of conditional swap regret: when all players follow conditional swap regret minimization strategies, then the empirical distribution approaches this equilibrium. Finally, we extend our results to the multi-armed bandit scenario. Theorem 2. There exists an online algorithm with bigram swap regret bounded as follows: Reg 2 T) O N T log N . Proof. |
| Researcher Affiliation | Collaboration | Mehryar Mohri Courant Institute and Google 251 Mercer Street New York, NY 10012 mohri@cims nyu edu Scott Yang Courant Institute 251 Mercer Street New York, NY 10012 yangs@cims nyu edu |
| Pseudocode | No | The paper describes the algorithm using numbered steps ('Our algorithm for achieving sublinear regret is defined as follows: 1. At t = 1, initialize N 2 external regret minimizing algorithms...'), but it does not present this as a formally labeled 'Pseudocode' or 'Algorithm' block with code-like formatting. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code (no links, no statements of code release). |
| Open Datasets | No | The paper is theoretical and does not describe experiments using publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |