A New PAC-Bayesian Perspective on Domain Adaptation
Authors: Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Then, we infer a learning algorithm and perform experiments on real data. |
| Researcher Affiliation | Academia | INRIA, SIERRA Project-Team, 75589, Paris, France, and D.I., Ecole Normale Superieure, 75230 Paris, France |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the DALC algorithm in prose and mathematical equations. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | Secondly, we evaluate DALC on the classical Amazon.com Reviews benchmark (Blitzer et al., 2006) |
| Dataset Splits | Yes | Each parameter is selected with a grid search thanks to a usual cross-validation (CV) on the source sample for SVM, and thanks to a reverse validation procedure11 (RCV) for CODA, DASVM, PBDA, and DALC. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn' but does not provide specific version numbers for it or any other ancillary software dependencies. |
| Experiment Setup | Yes | For a source S={(xi, yi)}ms i=1 and a target T={(x i)}mt i=1 samples of potentially different size, and some hyperparameters C>0, B>0, minimizing the next objective function w.r.t w R is equivalent to minimize the above bound. |