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Downstream performance prediction on MI (Midas) corrupted with Laplace noise

-0.894Downstream Performance

XGBoost + MLP

Updated 4d ago

Evaluation Results

Method	Links
XGBoost + MLP 2023.11		-0.894	2.64
XGBoost + MLP 2023.11		-0.896	2
Deep Lasso + MLP 2023.11		-0.898	2
AGL + MLP 2023.11		-0.9	3.36
Deep Lasso + MLP 2023.11		-0.9	1.91
Lasso + MLP 2023.11		-0.902	6.55
1L Lasso + MLP 2023.11		-0.902	3.09
Lasso + MLP 2023.11		-0.902	4.36
1L Lasso + MLP 2023.11		-0.902	3.73
RF + MLP 2023.11		-0.902	4.45
LassoNet + MLP 2023.11		-0.904	4.45
AGL + MLP 2023.11		-0.904	3.09
AM + MLP 2023.11		-0.905	3.91
RF + MLP 2023.11		-0.905	5
No FS + MLP 2023.11		-0.909	8.27
LassoNet + MLP 2023.11		-0.909	5.09
No FS + MLP 2023.11		-0.913	7.73
Univariate + MLP 2023.11		-0.92	7
Univariate + MLP 2023.11		-0.921	7.64