1,484 research outputs found
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.Comment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI
2018
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Estimating individual treatment effect: generalization bounds and algorithms
There is intense interest in applying machine learning to problems of causal
inference in fields such as healthcare, economics and education. In particular,
individual-level causal inference has important applications such as precision
medicine. We give a new theoretical analysis and family of algorithms for
predicting individual treatment effect (ITE) from observational data, under the
assumption known as strong ignorability. The algorithms learn a "balanced"
representation such that the induced treated and control distributions look
similar. We give a novel, simple and intuitive generalization-error bound
showing that the expected ITE estimation error of a representation is bounded
by a sum of the standard generalization-error of that representation and the
distance between the treated and control distributions induced by the
representation. We use Integral Probability Metrics to measure distances
between distributions, deriving explicit bounds for the Wasserstein and Maximum
Mean Discrepancy (MMD) distances. Experiments on real and simulated data show
the new algorithms match or outperform the state-of-the-art.Comment: Added name "TARNet" to refer to version with alpha = 0. Removed sup
Information-Theoretic Analysis of Unsupervised Domain Adaptation
This paper uses information-theoretic tools to analyze the generalization
error in unsupervised domain adaptation (UDA). We present novel upper bounds
for two notions of generalization errors. The first notion measures the gap
between the population risk in the target domain and that in the source domain,
and the second measures the gap between the population risk in the target
domain and the empirical risk in the source domain. While our bounds for the
first kind of error are in line with the traditional analysis and give similar
insights, our bounds on the second kind of error are algorithm-dependent, which
also provide insights into algorithm designs. Specifically, we present two
simple techniques for improving generalization in UDA and validate them
experimentally
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