A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling
Authors: Yiting Cao, Chao Lan
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show they converge efficiently as k increases and outperform their model-specific counterparts including random fourier feature, random vector functional link and extra tree on real-world data sets. |
| Researcher Affiliation | Academia | School of Computer Science, University of Oklahoma, Norman, OK, USA. Correspondence to: Chao Lan <clan@ou.edu>. |
| Pseudocode | Yes | Framework 1 The RHSS-based Learning Framework |
| Open Source Code | Yes | The codes of all experiments are available at https://github.com/yxc827/RHSS. |
| Open Datasets | Yes | We compare the performance of the proposed RHSS-KRR, RHSS-MLP and RHSS-Tree with their existing randomized counterparts on three public real-world data sets, namely, Crime and Community, Adult and COMPAS. |
| Dataset Splits | No | On each data set, we use the first half of the instances for training and the other half for testing. |
| Hardware Specification | No | The paper does not explicitly mention specific hardware details such as GPU/CPU models, processors, or memory used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For RHSS-KRR, the hypothesis coefficients are sampled independently from N(0, 1). ... the regularization coefficient is optimized to 0.1 on Crime and 0.001 on the other two data sets. ... single-hidden-layer architecture with 20 hidden neurons and ReLU activation function. ... regularization coefficient is optimized to 10. ... all non-optimized parameters are independently sampled from N(0, 1). ... bootstrap sample size is set to 80% of the original training set. ... k is the projected dimension of RP, and we fix the number of sampled hypotheses to 100 for RHSS-KRR. For RP, all projection entries are independently sampled from N(0, 0.01). |