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About this paper
Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
This research from Google Research, Tel Aviv University, and New York University advances domain adaptation theory by defining a generalized "discrepancy distance" applicable to diverse loss functions and tasks like regression. It derives novel generalization bounds and develops polynomial-time algorithms (Linear Program for classification, Semidefinite Program for regression) that minimize this empirical discrepancy, leading to improved target domain performance in initial experiments.
Key insights
- The discrepancy distance, defined as $ ext{disc}_L(Q_1, Q_2) = ext{max}_{h,h' ext{ in } H} |L_{Q_1}(h', h) - L_{Q_2}(h', h)|$, effectively generalizes distribution similarity beyond 0-1 classification.
- Data-dependent Rademacher complexity bounds allow for reliable estimation of the discrepancy distance from finite samples, making the theoretical framework empirically applicable.
- Minimizing the empirical discrepancy between source and target distributions can be formulated as solvable convex optimization problems (LP for classification, SDP for regression), providing a direct algorithmic path from theory to practice.
Summary by alphaXiv.
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