A POMDP Formulation of Proactive Learning
Authors: Kyle Wray, Shlomo Zilberstein
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is compared with the original three algorithms proposed by Donmez and Carbonell and a simple baseline. We demonstrate that our approach matches or improves upon the original approach within five different oracle scenarios, each on two datasets. |
| Researcher Affiliation | Academia | Kyle Hollins Wray and Shlomo Zilberstein College of Information and Computer Sciences University of Massachusetts Amherst, MA 01003, USA {wray,shlomo}@cs.umass.edu |
| Pseudocode | Yes | Algorithm 1 Proactive Learning POMDP: Initially unknown oracles; thus, it requires clustering. |
| Open Source Code | No | Finally, we will provide our source code so that others may more easily build upon this work to design and implement high caliber proactive learners. |
| Open Datasets | Yes | We create similar experiments to those of Donmez and Carbonell for two of the well-known UCI datasets they used: Adult and Spambase (Lichman 2013). ... Lichman, M. 2013. UCI machine learning repository. URL: http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | The paper mentions using a "test set" and |
| Hardware Specification | Yes | Our implementation uses Python 3.4.3 with scikit-learn 0.16.1, Num Py 1.9.2, and Sci Py 0.15.1, run on an Intel(R) Core(TM) i7-4702HQ CPU at 2.20GHz, 8GB of RAM, and a Nvidia(R) Ge Force GTX 870M. |
| Software Dependencies | Yes | Our implementation uses Python 3.4.3 with scikit-learn 0.16.1, Num Py 1.9.2, and Sci Py 0.15.1, run on an Intel(R) Core(TM) i7-4702HQ CPU at 2.20GHz, 8GB of RAM, and a Nvidia(R) Ge Force GTX 870M. We leverage a high-performing GPU-based implementation of PBVI using CUDA(C) 6.5 (Wray and Zilberstein 2015a; 2015b). |
| Experiment Setup | Yes | PBVI uses horizon 2du (increasing it only improves performances) and discount factor 0.9. |