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.
Robustness Guarantees for Bayesian Inference with Gaussian Processes
Authors: Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Andrea Patane7759-7768
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,286 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,501 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 evaluate our techniques on two examples, a GP regression problem and a fully-connected deep neural network, where we rely on weak convergence to GPs to study adversarial examples on the MNIST dataset. |
| Researcher Affiliation | Collaboration | 1Microsoft Research Cambridge, 2University of Oxford |
| Pseudocode | No | The paper describes algorithmic methods, but it does not include a clearly labeled pseudocode block or algorithm steps in a structured format. |
| Open Source Code | Yes | Code available at: https://github.com/andreapatane/checkGP. |
| Open Datasets | Yes | we train a selection of Re LU GPs on a subset of the MNIST dataset |
| Dataset Splits | No | The paper discusses training on a subset of the MNIST dataset using least-square classification and notes "Classification accuracy obtained on the full MNIST test set". It mentions using 100 to 2000 training samples, but does not specify a validation split or how one would reproduce it. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). It mentions using SIFT and discusses GPs and NNs, but without versioned software details. |
| Experiment Setup | Yes | Unless otherwise stated, we perform analysis on the best model obtained using 1000 training samples, that is, a two-hidden-layer architecture with σ2 w = 3.19 and σ2 b = 0.00. |