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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient and Flexible Inference for Stochastic Systems
Authors: Stefan Bauer, Nico S. Gorbach, Djordje Miladinovic, Joachim M. Buhmann
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our approach on two established benchmark models for stochastic systems especially used for weather forecasts. The runtime for state estimation using the approach of Vrettas et al. [2011] and our method is indicated in table 1. We use our method to infer the states and drift parameters for the Lorenz attractor where the dimension y is unobserved. The estimated state trajectories are shown in ๏ฌgure 4. |
| Researcher Affiliation | Academia | Stefan Bauer Department of Computer Science ETH Zurich EMAIL S. Gorbach Department of Computer Science ETH Zurich EMAIL.chรor de Miladinovi c Department of Computer Science ETH Zurich EMAIL M. Buhmann Department of Computer Science ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1 Ensemble based parameter estimation for SDEs |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in this paper was found. |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was provided. The paper refers to benchmark models (Lorenz96, Lorenz attractor) which are simulated, not datasets with specified public access. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was provided. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) were provided. |
| Experiment Setup | No | No specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) were found in the main text. |