Algorithms and Theory for Multiple-Source Adaptation (1805.08727v1)

Abstract: This work includes a number of novel contributions for the multiple-source adaptation problem. We present new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. Moreover, we give new algorithms for determining the distribution-weighted combination solution for the cross-entropy loss and other losses. We report the results of a series of experiments with real-world datasets. We find that our algorithm outperforms competing approaches by producing a single robust model that performs well on any target mixture distribution. Altogether, our theory, algorithms, and empirical results provide a full solution for the multiple-source adaptation problem with very practical benefits.

• The paper introduces normalized solutions with theoretical guarantees for combining multiple source domains, addressing challenges like varying conditional probabilities.
• It proposes novel distribution-weighted combination algorithms that perform effectively across various target mixture distributions.
• Empirical validation shows the proposed algorithms outperform existing approaches on real-world datasets like sentiment analysis and object recognition.

Overview of "Algorithms and Theory for Multiple-Source Adaptation"

The paper "Algorithms and Theory for Multiple-Source Adaptation" by Judy Hoffman, Mehryar Mohri, and Ningshan Zhang, presents a comprehensive set of theoretical contributions addressing the Multiple-Source Adaptation (MSA) problem. The authors focus on constructing robust learning models that integrate information from various source domains to achieve optimal performance in unknown target domains, thereby addressing prevalent challenges in tasks like speech recognition, object recognition, and sentiment analysis.

Key Contributions

1. Normalized Solutions: The paper introduces normalized solutions with significant theoretical guarantees for prevalent loss functions such as cross-entropy loss. These guarantees apply even when conditional probabilities differ across source domains.
2. Distribution-Weighted Combination: The authors propose novel algorithms for computing distribution-weighted combinations, demonstrating that these combinations perform well across varied target mixture distributions.
3. Empirical Validation: Through extensive experiments utilizing real-world datasets, the study finds that their algorithms outperform existing approaches, yielding models that generalize effectively to any target mixture distribution.

Theoretical Implications

The paper extends previous work by Mansour, Mohri, and Rostamizadeh, offering robust solutions for the MSA problem in stochastic settings. This is accomplished by incorporating distribution-weighted combination rules suitable for a variety of loss functions. The theoretical advancements provide solid foundations for practical benefits in multiple-source learning scenarios, particularly where conditional probabilities between domains may vary subtly.

Experimental Insights

The empirical section evidences the practical applicability of the proposed algorithms. The experiments cover domains such as sentiment analysis and object recognition, showing enhanced performance over baseline models. Notably, the distribution-weighted approach proves especially effective even when the target domain is a composite of multiple source domains.

Future Directions

The methodologies proposed for MSA problem show promise for future developments in AI, particularly in adaptive learning systems and domain generalization challenges. The paper lays groundwork for advancements in unsupervised domain adaptation, potentially simplifying adaptation processes and further enhancing the performance of AI systems in practical, multifaceted environments.

This research contributes significantly to the ongoing development of robust multi-domain learning models, providing a thoughtful, theoretically sound approach to challenges in multiple-source adaptation. As AI continues to evolve, the insights and algorithms offered here will likely inform new innovations across domain adaptation and AI learning frameworks.