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.
SDP Relaxation with Randomized Rounding for Energy Disaggregation
Authors: Kiarash Shaloudegi, András György, Csaba Szepesvari, Wilsun Xu
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,808 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,247 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 | 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. |