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Poster
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li · Hao Liu · Changyou Chen · Yuchen Pu · Liqun Chen · Ricardo Henao · Lawrence Carin

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #114
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We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.

Keywords: Semi-Supervised Learning · Deep Learning · Unsupervised Learning · Adversarial Networks · Generative Models
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Author Information

Chunyuan Li (Duke University)

Chunyuan is a PhD student at Duke University, affiliated with department of Electrical and Computer Engineering, advised by Prof. Lawrence Carin. His recent research interests focus on scalable Bayesian methods for deep learning, including generative models and reinforcement learning, with applications to computer vision and natural language processing.

Hao Liu (Nanjing University)
Changyou Chen (University at Buffalo)
Yuchen Pu (Duke University)
Liqun Chen (Duke University)
Ricardo Henao (Duke University)
Lawrence Carin (Duke University)

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