Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A POMDP Formulation of Proactive Learning
Authors: Kyle Wray, Shlomo Zilberstein
AAAI 2016 | Venue PDF | 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 EMAIL |
| 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. |