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].
On Measure Concentration of Random Maximum A-Posteriori Perturbations
Authors: Francesco Orabona, Tamir Hazan, Anand Sarwate, Tommi Jaakkola
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments We evaluated our approach on a 100 x 100 spin glass model with n = 10^4 variables, for which... |
| Researcher Affiliation | Academia | Francesco Orabona EMAIL Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave, Chicago, IL 60637; Tamir Hazan EMAIL Dept. of Computer Science, University of Haifa, 31905 Haifa, Israel; Anand D. Sarwate EMAIL Rutgers University, Dept. of Electrical and Computer Engineering, 94 Brett Road, Piscataway, NJ 08854; Tommi S. Jaakkola EMAIL MIT CSAIL, Stata Center, Bldg 32-G470, 77 Mass Ave. Cambridge, MA 02139 |
| Pseudocode | Yes | Algorithm 1 Sampling with low-dimensional random MAP perturbations from the Gibbs distribution (Hazan et al., 2013b) |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating data for the experiments ("The local field parameters θi were drawn uniformly at random from [−1, 1]... The parameters θi,j were drawn uniformly from [0, c]") but does not use a publicly available dataset with concrete access information or provide details for its own generated dataset. |
| Dataset Splits | No | The paper describes the experimental setup and the generation of data, but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We evaluated our approach on a 100 x 100 spin glass model with n = 10^4 variables, for which θ(x1, ..., xn) = Σi∈V θi(xi) + Σ(i,j)∈E θi,j(xi, xj). ... The local field parameters θi were drawn uniformly at random from [−1, 1]... The parameters θi,j were drawn uniformly from [0, c], where c ∈ [0, 4]... We evaluated the expected value of F(Γ) with 100 different samples of Γ. |