A scaled Bregman theorem with applications
Authors: Richard Nock, Aditya Menon, Cheng Soon Ong
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on each of these domains validate the analyses and suggest that the scaled Bregman theorem might be a worthy addition to the popular handful of Bregman divergence properties that have been pervasive in machine learning. We present some experiments validating our theoretical analysis for the applications above. |
| Researcher Affiliation | Collaboration | Data61, the Australian National University and the University of Sydney {richard.nock, aditya.menon, chengsoon.ong}@data61.csiro.au |
| Pseudocode | No | The paper provides mathematical equations for algorithms but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | No | The paper mentions simulating data ('we simulate on T0S2 a mixture of spherical Gaussian and uniform densities') or using the experimental setting of a prior work [20], but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes general experimental settings and parameters used for simulations (e.g., 'we simulate on T0S2 a mixture of spherical Gaussian and uniform densities in random rectangles with 2k components', 'misestimation factor ρ') but does not provide specific experimental setup details such as concrete hyperparameter values, optimizer settings, or detailed training configurations for any models. |