The Benefits of Learning with Strongly Convex Approximate Inference
Authors: Ben London, Bert Huang, Lise Getoor
ICML 2015 | Conference PDF | Archive PDF | Plain Text | 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 BLONDON@CS.UMD.EDU University of Maryland, College Park, MD 20742 USA Bert Huang BHUANG@VT.EDU Virginia Tech, Blacksburg, VA 24061 USA Lise Getoor GETOOR@SOE.UCSC.EDU 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}... |