On-the-Job Learning with Bayesian Decision Theory
Authors: Keenon Werling, Arun Tejasvi Chaganty, Percy S. Liang, Christopher D. Manning
NeurIPS 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested our approach on three datasets named-entity recognition, sentiment classification, and image classification. |
| Researcher Affiliation | Academia | Keenon Werling Department of Computer Science Stanford University EMAIL Chaganty Department of Computer Science Stanford University EMAIL Liang Department of Computer Science Stanford University EMAIL D. Manning Department of Computer Science Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Approximating expected utility with MCTS and progressive widening |
| Open Source Code | Yes | An open-source implementation of our system, dubbed LENSE for Learning from Expensive Noisy Slow Experts is available at http://www.github.com/keenon/lense. |
| Open Datasets | Yes | All code, data, and experiments for this paper are available on Coda Lab at https://www.codalab.org/worksheets/0x2ae89944846444539c2d08a0b7ff3f6f/. |
| Dataset Splits | Yes | All code, data, and experiments for this paper are available on Coda Lab at https://www.codalab.org/worksheets/0x2ae89944846444539c2d08a0b7ff3f6f/. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions software components and models like 'CRF prediction model' and 'Ada Grad', but it does not specify version numbers for any libraries or frameworks. |
| Experiment Setup | No | The paper describes the general experimental process and baselines but does not specify concrete hyperparameter values or detailed training configurations in the main text. |