Learning to Approximate a Bregman Divergence
Authors: Ali Siahkamari, XIDE XIA, Venkatesh Saligrama, David Castañón, Brian Kulis
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically validate our approach problems of ranking and clustering, showing that our method tends to outperform a wide range of linear and non-linear metric learning baselines. In this experiment we implement PBDL on four standard UCI classification data sets that have previously been used for metric learning benchmarking. See the supplementary material for additional data sets. We apply the learned divergences to the tasks of semi-supervised clustering and similarity ranking. |
| Researcher Affiliation | Academia | 1 Department of Electrical and Computer Engineering 2 Department of Computer Science Boston University Boston, MA, 02215 {siaa, xidexia, srv, dac, bkulis}@bu.edu |
| Pseudocode | No | The paper describes a linear program (LP) and refers to "the above algorithm" but does not provide a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code for all experiments is available on our github page2. 2https://github.com/Siahkamari/Learning-to-Approximate-a-Bregman-Divergence.git |
| Open Datasets | Yes | In this experiment we implement PBDL on four standard UCI classification data sets that have previously been used for metric learning benchmarking. See the supplementary material for additional data sets. |
| Dataset Splits | Yes | To learn a Bregman divergence we use a cross-validation scheme with 3 folds. The λ in our algorithm (PBDL) were both chosen by 3-fold cross validation on training data on a grid 10 8:1:4. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., GPU/CPU models, memory specifications, or cloud instance types). |
| Software Dependencies | No | The paper mentions "Gurobi solvers [13]" but does not specify a version number for Gurobi or other key software dependencies with their versions. |
| Experiment Setup | Yes | The λ in our algorithm (PBDL) were both chosen by 3-fold cross validation on training data on a grid 10 8:1:4. The number of inequalities provided was 2000 for each case. |