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.
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
Authors: Youngsoo Baek, Samuel Berchuck, Sayan Mukherjee
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,338 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,414 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 | Numerical simulations illustrate finer distributional properties of the two quantities for finite dimensions. We conjecture they have Gaussian fluctuations and exhibit similar properties as found by previous authors in a Gaussian sequence model, which is of independent theoretical interest. |
| Researcher Affiliation | Academia | Youngsoo Baek Department of Statistical Science Duke University Durham, NC 27705 EMAIL Samuel I. Berchuck Department of Biostatistics & Bioinformatics Duke University Durham, NC 27705 EMAIL Sayan Mukherjee Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig 04105 EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | MATLAB codes used to produce simulation results are included in the Supplementary Materials. |
| Open Datasets | No | The paper describes generating synthetic data for simulations using a "noiseless linear model y = x, β (||β|| = 1, ρ = )" and does not refer to any external public datasets or provide access information for specific training data. |
| Dataset Splits | No | The paper describes simulation setups and discusses training, but does not provide specific train/validation/test dataset splits as it generates synthetic data for its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions "MATLAB codes" are included in supplementary materials, but does not specify any version numbers for MATLAB or other software dependencies. |
| Experiment Setup | Yes | Figure 1: Comparison of asymptotic formula and 20 instances of S2 RF (12). Data are generated via noiseless linear model y = x, β (||β|| = 1, ρ = ). Activation is Re LU: σ(x) = max{0, x}. d and n are fixed to 100 and 300, respectively. The asymptotic formula for RRF (8) is plotted for comparison (red, dashed). Figure 2: Ratio of R(λopt) to S2(λopt) τ 2 as a function of ψ1 (2a) and of ψ2 (2b). In each plot, ψ2 and ψ1 are respectively fixed to 3, while F1 = 1, F = 0, and ρ = 1/τ 2 for noise variance τ 2 {.2, 5}. Figure 3: Histograms of 1e+4 draws of RRF and S2 RF τ 2 under low-noise linear model y = x, β + τ 2 with τ 2 = 1/5. |