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].
SDP Relaxation with Randomized Rounding for Energy Disaggregation
Authors: Kiarash Shaloudegi, András György, Csaba Szepesvari, Wilsun Xu
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results both in synthetic and real-world datasets demonstrate the superiority of our method. |
| Researcher Affiliation | Academia | Kiarash Shaloudegi Imperial College London EMAIL András György Imperial College London EMAIL Csaba Szepesvári University of Alberta EMAIL Wilsun Xu University of Alberta EMAIL |
| Pseudocode | Yes | Algorithm 1 ADMM-RR: Randomized rounding algorithm for suboptimal solution to (2) |
| Open Source Code | Yes | Our code is available online at https://github.com/kiarashshaloudegi/FHMM_inference. |
| Open Datasets | Yes | we used the REDD dataset of Kolter and Johnson [2011] |
| Dataset Splits | No | We use the first half of the data for training and the second half for testing. |
| Hardware Specification | No | The paper mentions memory usage (e.g., '14GB vs 6GB in our case') and notes implementation languages (Matlab, C++) and solvers (MOSEK), but does not specify hardware components like CPU/GPU models or total system memory. |
| Software Dependencies | No | The paper mentions using 'MOSEK inside the Matlab-based YALMIP', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Our method, shown under the label ADMM-RR, runs ADMM for 2500 iterations, runs the local search at the end of each 250 iterations, and chooses the result that has the maximum likelihood. |