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.
Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems
Authors: Xuezhi Wang, Junier B. Oliva, Jeff Schneider, Barnabás Póczos
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,933 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,695 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 | In addition, we illustrate our results with experiments on both simulated and real data. |
| Researcher Affiliation | Academia | Xuezhi Wang, Junier B. Oliva, Jeff Schneider, Barnab as P oczos Carnegie Mellon University, Pittsburgh, PA, USA EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The real dataset is the Air Quality Index (AQI) dataset [Mei et al., 2014]. |
| Dataset Splits | Yes | We estimate the risk at (n0, n1) by crossvalidating the set of projection coefficients and calculating the loss of the estimate, and taking the mean over the 100 instances of Y0 and Y1. ... We also plot the leave-one-out error for each task (loo-1 through 4)... |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We consider the cosine basis: '0(x) = 1, 'k(x) = √2 cos(πkx), ∀ k ≥ 1. ... We define f0(x) PM k=0 a(0) k 'k(x), by generating the projection coefficients a(0) k , M = 500. ... Specifically we consider n1 ∈ {15, 20, . . . , 50} and n0 = n2 1 . ... The risk estimation was performed for the values of γg ∈ {1.25, 1.75, 2.25} and γ0 = 1, keeping f0 constant throughout and changing g per value of γg (see Fig.3 (b-d)). ... The results are averaged over 20 experiments. |