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].
The Benefits of Learning with Strongly Convex Approximate Inference
Authors: Ben London, Bert Huang, Lise Getoor
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that learning with a strongly convex free energy, using our optimization framework to guarantee a given modulus, results in substantially more accurate marginal probabilities, thereby validating our theoretical claims and the effectiveness of our framework. |
| Researcher Affiliation | Academia | Ben London EMAIL University of Maryland, College Park, MD 20742 USA Bert Huang EMAIL Virginia Tech, Blacksburg, VA 24061 USA Lise Getoor EMAIL University of California, Santa Cruz, CA 95064 USA |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper mentions using third-party tools like Schmidt's LBFGS and UGM, and states 'we use our own implementation of counting number belief propagation (CBP)', but does not provide a link or explicit statement that their own code is open source. |
| Open Datasets | No | Our synthetic data generator is based on those used in prior work (e.g., Hazan & Shashua, 2008; Meshi et al., 2009) to evaluate approximate marginal inference. We generate data from an (8x8) non-toroidal grid-structured model... The paper describes generating synthetic data but does not provide access information for it. |
| Dataset Splits | No | The paper mentions using '100 joint assignments to Y' to 'train a model' but does not specify train/validation/test splits, percentages, or cross-validation details. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions using 'MATLAB', 'Mark Schmidt s Undirected Graphical Models (UGM) toolkit (2013b)', 'Schmidt s implementation of LBFGS with Wolfe line search (2013a)', and 'MATLAB s quadprog', but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | The regularization parameter, Λm, is set to 1/ m, per Proposition 1. We generate 20 models... For each value of ωs {0.05, 1} and ωp {0.1, 0.2, 0.5, 1, 2, 5}... |