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.
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert
Authors: Yoonhyung Lee, Sungdong Lee, Joong-Ho Won
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 42,201 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,600 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 | 6. Experiments Following Bach & Moulines (2011), we examined the convergence behavior of prox RM and prox PR using two univariate functions: L(θ) = 1/2θ^2 (strongly convex) and L(θ) = 1/4θ^4 (non-strongly convex)... Figs. 1 and 2 plot the squared estimation... Table 2 collects the results. Table 3 summarizes the results. |
| Researcher Affiliation | Collaboration | 1Kakao Entertainment Corp. 2Department of Statistics, Seoul National University. |
| Pseudocode | No | The paper describes algorithms using mathematical equations, e.g., 'θn = θn-1 - γn ℓ(Zn, θn)', but does not include structured 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | No explicit statement about making code open source or providing a link to a code repository was found. |
| Open Datasets | No | We generated Zn = (yn, xn) where yn = x^T nθ + ϵn, xn ~ N(0, Σ), and ϵn ~ N(0, 1)... we instead used a smoothed version... and let Z ~ N(0, 1). |
| Dataset Splits | No | The paper describes the generation of synthetic data and the number of iterations (e.g., '100 independent runs of n = 10^6 ISGD iterations'), but does not specify explicit training/validation/test dataset splits. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, memory, or cloud instance types) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that were used for the experiments. |
| Experiment Setup | Yes | We fixed the initial point θ0 = 10 for the quadratic and θ0 = 2 for the quartic function, and observed 100 independent runs of n = 10^6 ISGD iterations for initial step size γ1 {1/5, 1, 5, 20, 100} and exponent γ {1/5, 1/3, 2/5, 1/2, 2/3, 1}. We fixed θ = (1, . . . , 1)^T and ran n = 10^5 iterations of ISGD for γ {0.6, 1.0}, p {5, 20, 100, 200} with θ0 = 0 for each type of Σ. The n = 10^6 iterations were started with θ0 = 0 for each replication, where γ {0.6, 1} and µ {10^-1, 10^-2, 10^-3}; we used γ1 = 250 when γ = 1 and γ1 = 30 when γ = 0.6. |