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Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models.

, , , and . AAAI, page 9057-9065. AAAI Press, (2022)

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Domain-Adversarial Neural Networks., , , , and . CoRR, (2014)Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm., , , , and . J. Mach. Learn. Res., (2015)A PAC-Bayes Sample-compression Approach to Kernel Methods., , , , and . ICML, page 297-304. Omnipress, (2011)Invariant Causal Set Covering Machines., , , , and . CoRR, (2023)A Greedy Algorithm for Building Compact Binary Activated Neural Networks., and . CoRR, (2022)Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting., , , , , , and . ECML/PKDD (3), volume 12459 of Lecture Notes in Computer Science, page 141-157. Springer, (2020)PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations., , , , and . Canadian AI, Canadian Artificial Intelligence Association, (2023)A PAC-Bayes Risk Bound for General Loss Functions., , , and . NIPS, page 449-456. MIT Press, (2006)Domain-Adversarial Training of Neural Networks., , , , , , , and . J. Mach. Learn. Res., (2016)Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models., , , and . AAAI, page 9057-9065. AAAI Press, (2022)