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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Authors: Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on unweighted SAT instances and on weighted Markov Random Field models, comparing our algorithm with variational methods, as well as sample average approximation. We also show the effectiveness of our algorithm on applications in computer vision with deep neural networks and in computational sustainability. |
| Researcher Affiliation | Academia | Yexiang Xue Department of Computer Science Cornell University EMAIL Zhiyuan Li Institute of Interdisciplinary Information Sciences Tsinghua University lizhiyuan13@mails. Tsinghua.edu.cn Stefano Ermon Department of Computer Science Stanford University EMAIL Carla P. Gomes, Bart Selman Department of Computer Science Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1: XOR_Binary(w : A X {0, 1}, a0, k) ... Algorithm 2: XOR_K(w : A X {0, 1}, k, T) ... Algorithm 3: XOR_MMAP(w : A X {0, 1},n = log2 |X|,m = log2 |A|,T) |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We also apply the Marginal MAP solver to an image completion task. We ο¬rst learn a two-layer deep belief network [3, 10] from a 14-by-14 MNIST dataset. |
| Dataset Splits | No | The paper mentions using the MNIST dataset and applying the solver to an image completion task, but it does not provide explicit details about training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions computational constraints like 'a 4G memory limit' and 'a one-hour time and a 4G memory limit', but it does not specify any particular hardware components such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'Mixed Integer Programming (MIP)', 'an exact counter ACE [5]', 'Mixed Loopy Belief Propagation (Mixed LBP) [13] implementation', and a 'two-layer deep belief network [3, 10]', but it does not specify any version numbers for these software components or frameworks. |
| Experiment Setup | Yes | We tune the constants of our XOR_MMAP so it gives a 2^10 = 1024-approximation (2^-5 sol <= OPT <= 2^5 sol, Ξ΄ = 10^-3). SAA uses 10,000 samples. For SAA, we use 1,000 samples, which is the largest we can use within the memory limit. All algorithms are given a one-hour time and a 4G memory limit. |