2020/08/25 by Corinna Cortes, Mehryar Mohri, Cortes, Corinna +5 · 6 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Adaptation (eye) #Algorithm #Artificial intelligence #Cancer-related molecular mechanisms research #Computer science #Conditional probability distribution #Density estimation #Discriminative model #Domain (mathematical analysis) #Domain Adaptation and Few-Shot Learning #Domain adaptation #FOS: Computer and information sciences #Generative grammar #Generative model #Kernel (algebra) #Kernel density estimation #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Multimodal Machine Learning Applications #Pattern recognition (psychology) #Statistics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2008.11036
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/08/25 · arxiv created 2021/02/12 · arxiv updated 2021/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a new discriminative technique for the multiple-source adaptation, MSA, problem. Unlike previous work, which relies on density estimation for each source domain, our solution only requires conditional probabilities that can easily be accurately estimated from unlabeled data from the source domains. We give a detailed analysis of our new technique, including general guarantees based on Rényi divergences, and learning bounds when conditional Maxent is used for estimating conditional probabilities for a point to belong to a source domain. We show that these guarantees compare favorably to those that can be derived for the generative solution, using kernel density estimation. Our experiments with real-world applications further demonstrate that our new discriminative MSA algorithm outperforms the previous generative solution as well as other domain adaptation baselines.