A* Sampling
Authors: Chris J Maddison, Daniel Tarlow, Tom Minka
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms. There are three main aims in this section. First, understand the empirical behavior of A sampling as parameters of the inference problem and o(x) bounds vary. Second, demonstrate generality by showing that A sampling algorithms can be instantiated in just a few lines of model-specific code by expressing o(x) symbolically, and then using a branch and bound library to automatically compute bounds. Finally, compare to OS and an MCMC method (slice sampling). |
| Researcher Affiliation | Collaboration | Chris J. Maddison Dept. of Computer Science University of Toronto cmaddis@cs.toronto.edu Daniel Tarlow, Tom Minka Microsoft Research {dtarlow,minka}@microsoft.com |
| Pseudocode | Yes | Algorithm 1 Top-Down Construction; Algorithm 2 A Sampling |
| Open Source Code | No | The paper does not provide any statements or links indicating that the source code for the described methodology (A Sampling) is open or publicly available. It only mentions a third-party tool: "Func Designer. http://openopt.org/Func Designer, June 2014." |
| Open Datasets | No | The paper describes generating its own data for experiments (e.g., "generated a small (N=3) set of training data from the model", "We generated N data points..."). It does not mention using or providing access to any publicly available or open datasets. |
| Dataset Splits | No | The paper mentions using "training data" (e.g., "generated a small (N=3) set of training data from the model") but does not provide any specific details about how the dataset was split into training, validation, and test sets, or specify exact percentages, sample counts, or refer to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using "Func Designer [17]" but does not provide a specific version number for it or any other software dependencies crucial for replication. |
| Experiment Setup | No | The paper describes the problems used for experiments and some general setup details (e.g., "We set uniform priors from a reasonable range for all parameters", "The model in all cases is yn = f(xn) + n where n is the data point index and n is Gaussian noise"), but it does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or explicit training configurations for the A sampling algorithm. |