Sampling from Probabilistic Submodular Models
Authors: Alkis Gotovos, Hamed Hassani, Andreas Krause
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also evaluate the efficiency of the Gibbs sampler on three examples of such models, and compare against a recently proposed variational approach. 5 Experiments We compare the Gibbs sampler against the variational approach proposed by Djolonga and Krause [6] for performing inference in models of the form (1), and use the same three models as in their experiments. Figure 1 compares the average absolute error of the approximate marginals with respect to the exact ones. |
| Researcher Affiliation | Academia | Alkis Gotovos ETH Zurich alkisg@inf.ethz.ch S. Hamed Hassani ETH Zurich hamed@inf.ethz.ch Andreas Krause ETH Zurich krausea@ethz.ch |
| Pseudocode | Yes | Algorithm 1 Gibbs sampler Input: Ground set V , distribution p(S) exp(βF(S)) 1: X0 random subset of V 2: for t = 0 to Niter do 3: v Unif(V ) 4: F (v|Xt) F(Xt {v}) F(Xt \ {v}) 5: padd exp(β F (v|Xt))/(1 + exp(β F (v|Xt))) 6: z Unif([0, 1]) 7: if z padd then Xt+1 Xt {v} else Xt+1 Xt \ {v} 8: end for |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the methodology described. |
| Open Datasets | Yes | The model is constructed from randomly subsampling real data from a problem of sensor placement in a water distribution network [22]. |
| Dataset Splits | No | The paper describes discarding samples as 'burn-in' for the Gibbs sampler, which is an MCMC practice, but does not provide details on traditional training/validation dataset splits used for model learning or hyperparameter tuning. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We run the Gibbs sampler for 100, 500, and 2000 iterations on each problem instance. In compliance with recommended MCMC practice [11], we discard the first half of the obtained samples as burn-in, and only use the second half for estimating the marginals. |