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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A New PAC-Bayesian Perspective on Domain Adaptation
Authors: Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
ICML 2016 | Venue PDF | 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. |