Research map / Learning theory and algorithms / Combinatorial and search optimization
SDP Relaxation with Randomized Rounding for Energy Disaggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| ADMM-RR (SDP relaxation + rounding) instead of KJ | FHMM inference · REDD | average precision (%): 38.68 -> 60.97 | Helped |
| ADMM-RR instead of ZGS | FHMM inference · REDD | average precision (%): 17.97 -> 60.97 | Helped |
| ADMM-RR instead of KJ | FHMM inference · REDD | average recall: 75.02% -> 78.56% | Helped |
| ADMM-RR instead of ZGS | FHMM inference · REDD | average recall: 36.22% -> 78.56% | Helped |
| ADMM-RR instead of KJ | FHMM inference · REDD | energy assignment error: 4.44% -> 2.87% | Helped |
| ADMM-RR instead of ZGS | FHMM inference · REDD | energy assignment error: 3.94% -> 2.87% | Helped |
| ADMM-RR instead of KJ | FHMM inference · REDD, Microwave | precision / recall: 12.40/74.74% vs 13.40/96.32% | Mixed |
| ADMM-RR instead of KJ | FHMM inference · REDD, Wash./Dry.-20-B | recall: 77.10% -> 28.31% | Hurt |
| ADMM-RR instead of KJ | FHMM inference · REDD, Unregistered-A | recall: 25.91% -> 87.80% | Helped |
| ADMM-RR vs KJ/ZGS in memory | FHMM inference · REDD | memory usage: 14GB -> 6GB | Helped |
| ADMM-RR (Matlab) vs YALMIP QP of KJ/ZGS | FHMM inference · REDD | runtime: 5 minutes -> 5 hours | Hurt |
| ADMM-RR instead of ZGS | FHMM inference · REDD, Fridge energy error | error: 0.44% -> 0.98% | Hurt |
Closest papers by meaning
- Inverse Filtering for Hidden Markov Models (NeurIPS 2017 · similarity 0.84)
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- Variational BOLT: Approximate Learning in Factorial Hidden Markov Models With Application to Energy Disaggregation (AAAI 2018 · similarity 0.83)
- Continuous Relaxation of MAP Inference: A Nonconvex Perspective (CVPR 2018 · similarity 0.82)
- Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces (NeurIPS 2024 · similarity 0.81)
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