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 [1].
Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Authors: Qi Lou, Rina Dechter, Alexander T. Ihler
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach empirically on real-world problem instances taken from recent UAI competitions. |
| Researcher Affiliation | Academia | Qi Lou Computer Science Univ. of California, Irvine Irvine, CA 92697, USA EMAIL Rina Dechter Computer Science Univ. of California, Irvine Irvine, CA 92697, USA EMAIL Alexander Ihler Computer Science Univ. of California, Irvine Irvine, CA 92697, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Dynamic importance sampling (DIS) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | Our benchmarks include pedigree, 22 genetic linkage instances from the UAI 08 inference challenge1; protein, 50 randomly selected instances made from the small protein side-chains of [22]; and BN, 50 randomly selected Bayesian networks from the UAI 06 competition2. 1http://graphmod.ics.uci.edu/uai08/Evaluation/Report/Benchmarks/ 2http://melodi.ee.washington.edu/~bilmes/uai06Inference Evaluation/ |
| Dataset Splits | No | The paper mentions evaluating on 'real-world problem instances from recent UAI competitions' and different benchmarks, but it does not specify explicit training, validation, or test dataset splits used for their experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper states 'All implementations are in C/C++', but does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers). |
| Experiment Setup | Yes | We alloted 1GB memory to all methods, ๏ฌrst computing the largest ibound that ๏ฌts the memory budget, and using the remaining memory for search. ... We show DIS for two settings, (Nl=1, Nd=1) and (Nl=1, Nd=10), balancing the effort between search and sampling. ... We set ฮด = 0.025 and ran each algorithm for 1 hour. |