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cs.LG2025
A Planning Framework for Adaptive Labeling
Daksh Mittal, Yuanzhe Ma, Shalmali Joshi +1
Ground truth labels/outcomes are critical for advancing scientific and engineering applications, e.g., evaluating the treatment effect of an intervention or performance of a predic…
cs.LG2021
Hybrid Random Features
Krzysztof Choromanski, Haoxian Chen, Han Lin +10
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel es…