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].
Fast Learning from Distributed Datasets without Entity Matching
Authors: Giorgio Patrini, Richard Nock, Stephen Hardy, Tiberio Caetano
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, We have evaluated the leverage that DRL provides compared to the peers..., We ran experiments on a dozen UCI domains., Table 3: Results on domain ionosphere..., Table 4: Results on domain musk... |
| Researcher Affiliation | Collaboration | Australian National University1, NICTA2, Ambiata3, University of New South Wales4 |
| Pseudocode | Yes | Algorithm 1 RADOCRAFT(P1, P2, ..., Pp), Algorithm 2 DRL(P1, P2, ..., Pp; Γ) |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing the source code for the described methodology, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We ran experiments on a dozen UCI domains. |
| Dataset Splits | Yes | Each peer Pj estimates learns through a ten-folds stratified cross-validation (CV) minimization of sql(Sj, ; γ Iddj)..., CV is performed on rados as follows: first, RB is split in 10 folds, RB, , for = 1, 2, ..., 10. |
| 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 | Yes | γ is optimized by a 10-folds CV on I ., We have carried out a very simple optimisation of the regularisation matrix of DRL as a diagonal matrix which weights differently the shared features, Γ .= Diag(lift X(proj J(1)))+ γ Diag(lift X(proj X\J(1))), for γ 2 G. |