Rounding-based Moves for Metric Labeling
Authors: M. Pawan Kumar
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that the multiplicative bound of a move-making algorithm exactly matches the approximation factor of the corresponding rounding procedure for any arbitrary distance function. Our analysis includes all known results for move-making algorithms as special cases. ... Our experiments (described in the technical report) confirm that the rounding-based move-making algorithms provide similar accuracy to the LP relaxation, while being significantly faster due to the use of efficient minimum st-cut solvers. |
| Researcher Affiliation | Academia | M. Pawan Kumar Ecole Centrale Paris & INRIA Saclay pawan.kumar@ecp.fr |
| Pseudocode | Yes | Algorithm 1 The complete rounding procedure. ... Algorithm 2 The complete move-making algorithm. ... Algorithm 3 The interval rounding procedure. ... Algorithm 4 The interval move-making algorithm. ... Algorithm 5 The hierarchical rounding procedure. ... Algorithm 6 The hierarchical move-making algorithm. |
| Open Source Code | No | The paper does not provide a specific repository link, explicit code release statement, or indicate code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper discusses theoretical aspects and algorithms for metric labeling. While it mentions "experiments (described in the technical report)", this document does not provide concrete access information for any publicly available or open dataset used for training. |
| Dataset Splits | No | The paper focuses on theoretical proofs and algorithm descriptions. It references experiments in a separate technical report but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning within this paper. |
| 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 algorithms and theoretical guarantees but does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. It references experiments in an accompanying technical report. |