Adaptive Classification for Prediction Under a Budget
Authors: Feng Nan, Venkatesh Saligrama
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost. We test various aspects of our algorithms and compare with stateof-the-art feature-budgeted algorithms on five real world benchmark datasets: Letters, Mini Boo NE Particle Identification, Forest Covertype datasets from the UCI repository [6], CIFAR-10 [11] and Yahoo! Learning to Rank[4]. |
| Researcher Affiliation | Academia | Feng Nan Systems Engineering Boston University Boston, MA 02215 fnan@bu.edu Venkatesh Saligrama Electrical Engineering Boston University Boston, MA 02215 srv@bu.edu |
| Pseudocode | Yes | Algorithm 1 ADAPT-LIN and Algorithm 2 ADAPT-GBRT |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or provide a link to a code repository. |
| Open Datasets | Yes | Letters, Mini Boo NE Particle Identification, Forest Covertype datasets from the UCI repository [6], CIFAR-10 [11] and Yahoo! Learning to Rank[4]. |
| Dataset Splits | Yes | Table 1: Dataset Statistics Dataset #Train #Validation #Test #Features Feature Costs |
| Hardware Specification | No | The paper does not explicitly state the specific hardware (e.g., GPU models, CPU models, or cloud instance types) used to run its experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms like 'logistic regression', 'gradient boosted trees', 'CART [2]', and 'RBF-SVM', but it does not specify version numbers for any of these software dependencies. |
| Experiment Setup | No | Detailed experiment setups can be found in the Suppl. Material. |