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.
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Authors: Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 20,043 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,473 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 this work, we propose a new method, BR-SNIS, whose complexity is essentially the same as that of SNIS and which significantly reduces bias without increasing the variance. This method is a wrapper in the sense that it uses the same proposal samples and importance weights as SNIS, but makes clever use of iterated sampling importance resampling (i-SIR) to form a bias-reduced version of the estimator. We furnish the proposed algorithm with rigorous theoretical results, including new bias, variance and high-probability bounds, and these are illustrated by numerical examples. |
| Researcher Affiliation | Academia | Gabriel Cardoso Centre de Mathématiques appliquées, Ecole polytechnique, IHU Liryc, fondation Bordeaux Université, Univ Bordeaux, CRCTB U4045, INSERM, EMAIL Sergey Samsonov HSE University Achille Thin UMR MIA, Agro Paris Tech, Eric Moulines Centre de Mathématiques appliquées, Ecole polytechnique, Jimmy Olsson Department of Mathematics, KTH Royal Institute of Technology. |
| Pseudocode | No | The paper describes algorithms (i-SIR, BR-SNIS) in text, but it does not provide any explicitly labeled "Pseudocode" block or "Algorithm" figure. |
| Open Source Code | Yes | The code used for this experiment is available at 1. https://github.com/gabrielvc/br_snis |
| Open Datasets | Yes | For numerical illustration, we use the heart failure clinical records (d = 13, T = 299), breast cancer detection (d = 30, T = 569), and Covertype (d = 55, T = 4 104) datasets from the UCI machine learning repository. ... As an illustration, we train the model using the binarized MNIST dataset [41] |
| Dataset Splits | No | The paper mentions the "MNIST validation set" in Table 2, but it does not specify the exact splits (percentages or counts) used for training, validation, or testing for any of the datasets. |
| Hardware Specification | No | The paper does not mention any specific GPU models, CPU models, or cloud computing instance types used for the experiments. It refers to "computational resources" generally. |
| Software Dependencies | No | The paper mentions using the "Adam optimizer [20]" but does not specify its version number or any other software dependencies with their versions (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For the algorithm BR-SNIS, we used N {129, 513}, k0 = kmax 1 and kmax = M/(N 1) bootstrap rounds. ... All models are run for 100 epochs, using the Adam optimizer [20] and a learning rate of 10 4. ... As a prior, we use a Gaussian distribution N(0, τ 2I) with τ 2 = 5 10 2. |