Notice: 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. Full accuracy metrics and methodology are described in [1].
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Authors: Adrien Corenflos, Nicolas Chopin, Simo Särkkä
JMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we experimentally demonstrate the statistical and computational properties of our method on a suite of examples. The article concludes with a discussion of the limitations and possible improvements of the de-Sequentialized Monte Carlo method. |
| Researcher Affiliation | Academia | Adrien Corenflos EMAIL Department of Electrical Engineering and Automation, Aalto University Nicolas Chopin EMAIL ENSAE, Institut Polytechnique de Paris Simo S arkk a EMAIL Department of Electrical Engineering and Automation, Aalto University |
| Pseudocode | Yes | Algorithm 1: Block combination ... Algorithm 2: Smoother initialization ... Algorithm 3: Recursion ... Algorithm 4: Conditional Block combination ... Algorithm 5: PIT linearized proposal smoother |
| Open Source Code | Yes | All the results were obtained using an Nvidia Ge Force RTX 3090 GPU with 24GB memory and the code to reproduce them can be found at https://github.com/Adrien Corenflos/parallel-ps. |
| Open Datasets | No | The paper refers to models from existing literature and states that datasets were 'generated from the model' (Section 5.1) or mentions 'the same prior and data (nutria, T + 1 = 120) as in these references' (Section 5.2), but does not provide explicit links, DOIs, or repositories for public access to the specific datasets used for their experiments. |
| Dataset Splits | No | The paper describes generating 50 datasets for different T values (T = 32, 64, 128, 256, 512) and repeating experiments 100 times on each generated dataset in Section 5.1. In Section 5.2, it refers to using 'data (nutria, T + 1 = 120) as in these references'. However, it does not specify any training/test/validation splits for these datasets. |
| Hardware Specification | Yes | All the results were obtained using an Nvidia Ge Force RTX 3090 GPU with 24GB memory and the code to reproduce them can be found at https://github.com/Adrien Corenflos/parallel-ps. |
| Software Dependencies | No | The paper mentions running experiments on a GPU and discusses parallelization, but it does not specify any software libraries, frameworks, or their version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | In Section 5.1, the experiments involved generating '50 datasets x0:T , y0:T from the model for T = 32, 64, 128, 256, 512 and repeat 100 d SMC and FFBS smoothing experiments on each dataset generated'. It also specifies 'N = 25 N = 50 N = 100 N = 250 N = 500 N = 1000' particles. Section 5.2 mentions 'N = 50 particles' and '25 iterations' for EKS. Section 5.3 states 'we take σ to be in {0.3, 0.4, 0.5}'. |