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.
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
Authors: Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,797 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,596 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 illustrate the efficiency of our novel implementation in experiments including neural network optimization and image synthesis. |
| Researcher Affiliation | Academia | 1Kyushu Institute of Technology 2Center for Advanced Intelligence Project 3University of Tokyo 4University of Toronto 5Vector Institute for Artificial intelligence. |
| Pseudocode | Yes | Algorithm 1 Discrete-time Entropic Fictitious Play; Algorithm 2 Efficient Implementation of EFP for Finite-sum Problem |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions generating data for a student-teacher setting and using the Mona Lisa image as a target, but does not provide specific access information (link, DOI, formal citation) for any publicly available datasets used for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We optimize the neural network using EFP with an outerloop step size ηγ = 0.01. At each iteration, we approximate the proximal Gibbs measure ˆµt via the Langevin Monte Carlo algorithm with step size η = 0.01. (Section 7.1) We run Algorithm 2 with λ = 10 5, λ = 10 4, T = 2000, S = 10, m = 1000, η γ = 0.01 to fit the target image. As for the step size for Langevin Monte Carlo, we used cosine annealing from 0.1 to 0.01. (Figure 3 caption) |