This paper addresses the problem of unsupervised domain adaption from theoretical and algorithmic perspectives. Existing domain adaptation theories naturally imply minimax optimization algorithms, which connect well with the domain adaptation methods based on adversarial learning. However, several disconnections still exist and form the gap between theory and algorithm. We extend previous theories (Mansour et al., 2009c; Ben-David et al., 2010) to multiclass classification in domain adaptation, where classifiers based on the scoring functions and margin loss are standard choices in algorithm design. We introduce Margin Disparity Discrepancy, a novel measurement with rigorous generalization bounds, tailored to the distribution comparison with the asymmetric margin loss, and to the minimax optimization for easier training. Our theory can be seamlessly transformed into an adversarial learning algorithm for domain adaptation, successfully bridging the gap between theory and algorithm. A series of empirical studies show that our algorithm achieves the state of the art accuracies on challenging domain adaptation tasks.
Author Information
Yuchen Zhang (Tsinghua University)
Tianle Liu (Tsinghua University)
Mingsheng Long (Tsinghua University)
Michael Jordan (UC Berkeley)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Oral: Bridging Theory and Algorithm for Domain Adaptation »
Thu Jun 13th 09:40 -- 10:00 AM Room Room 103
More from the Same Authors
-
2019 Poster: Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers »
Hong Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Poster: Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation »
Kaichao You · Ximei Wang · Mingsheng Long · Michael Jordan -
2019 Poster: Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation »
Xinyang Chen · Sinan Wang · Mingsheng Long · Jianmin Wang -
2019 Poster: A Dynamical Systems Perspective on Nesterov Acceleration »
Michael Muehlebach · Michael Jordan -
2019 Poster: Theoretically Principled Trade-off between Robustness and Accuracy »
Hongyang Zhang · Yaodong Yu · Jiantao Jiao · Eric Xing · Laurent El Ghaoui · Michael Jordan -
2019 Oral: A Dynamical Systems Perspective on Nesterov Acceleration »
Michael Muehlebach · Michael Jordan -
2019 Oral: Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation »
Kaichao You · Ximei Wang · Mingsheng Long · Michael Jordan -
2019 Oral: Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation »
Xinyang Chen · Sinan Wang · Mingsheng Long · Jianmin Wang -
2019 Oral: Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers »
Hong Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Oral: Theoretically Principled Trade-off between Robustness and Accuracy »
Hongyang Zhang · Yaodong Yu · Jiantao Jiao · Eric Xing · Laurent El Ghaoui · Michael Jordan -
2019 Poster: On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms »
Tianyi Lin · Nhat Ho · Michael Jordan -
2019 Poster: Rao-Blackwellized Stochastic Gradients for Discrete Distributions »
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe -
2019 Oral: Rao-Blackwellized Stochastic Gradients for Discrete Distributions »
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe -
2019 Oral: On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms »
Tianyi Lin · Nhat Ho · Michael Jordan -
2018 Poster: On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo »
Niladri S Chatterji · Nicolas Flammarion · Yian Ma · Peter Bartlett · Michael Jordan -
2018 Poster: RLlib: Abstractions for Distributed Reinforcement Learning »
Eric Liang · Richard Liaw · Robert Nishihara · Philipp Moritz · Roy Fox · Ken Goldberg · Joseph Gonzalez · Michael Jordan · Ion Stoica -
2018 Oral: On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo »
Niladri S Chatterji · Nicolas Flammarion · Yian Ma · Peter Bartlett · Michael Jordan -
2018 Oral: RLlib: Abstractions for Distributed Reinforcement Learning »
Eric Liang · Richard Liaw · Robert Nishihara · Philipp Moritz · Roy Fox · Ken Goldberg · Joseph Gonzalez · Michael Jordan · Ion Stoica -
2018 Poster: SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate »
Aaditya Ramdas · Tijana Zrnic · Martin Wainwright · Michael Jordan -
2018 Oral: SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate »
Aaditya Ramdas · Tijana Zrnic · Martin Wainwright · Michael Jordan -
2018 Poster: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation »
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan -
2018 Poster: PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning »
Yunbo Wang · Zhifeng Gao · Mingsheng Long · Jianmin Wang · Philip Yu -
2018 Oral: PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning »
Yunbo Wang · Zhifeng Gao · Mingsheng Long · Jianmin Wang · Philip Yu -
2018 Oral: Learning to Explain: An Information-Theoretic Perspective on Model Interpretation »
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan -
2017 Poster: How to Escape Saddle Points Efficiently »
Chi Jin · Rong Ge · Praneeth Netrapalli · Sham Kakade · Michael Jordan -
2017 Talk: How to Escape Saddle Points Efficiently »
Chi Jin · Rong Ge · Praneeth Netrapalli · Sham Kakade · Michael Jordan -
2017 Poster: Deep Transfer Learning with Joint Adaptation Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2017 Poster: Breaking Locality Accelerates Block Gauss-Seidel »
Stephen Tu · Shivaram Venkataraman · Ashia Wilson · Alex Gittens · Michael Jordan · Benjamin Recht -
2017 Talk: Deep Transfer Learning with Joint Adaptation Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2017 Talk: Breaking Locality Accelerates Block Gauss-Seidel »
Stephen Tu · Shivaram Venkataraman · Ashia Wilson · Alex Gittens · Michael Jordan · Benjamin Recht