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Sethuraman Panchanathan

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3

ICML Conference 2013 Conference Paper

Joint Transfer and Batch-mode Active Learning

  • Rita Chattopadhyay
  • Wei Fan 0001
  • Ian Davidson
  • Sethuraman Panchanathan
  • Jieping Ye

Active learning and transfer learning are two different methodologies that address the common problem of insufficient labels. Transfer learning addresses this problem by using the knowledge gained from a related and already labeled data source, whereas active learning focuses on selecting a small set of informative samples for manual annotation. Recently, there has been much interest in developing frameworks that combine both transfer and active learning methodologies. A few such frameworks reported in literature perform transfer and active learning in two separate stages. In this work, we present an integrated framework that performs transfer and active learning simultaneously by solving a single convex optimization problem. The proposed framework computes the weights of source domain data and selects the samples from the target domain data simultaneously, by minimizing a common objective of reducing distribution difference between the data set consisting of reweighted source and the queried target domain data and the set of unlabeled target domain data. Comprehensive experiments on three real world data sets demonstrate that the proposed method improves the classification accuracy by 5% to 10% over the existing two-stage approach

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NeurIPS Conference 2011 Conference Paper

A Two-Stage Weighting Framework for Multi-Source Domain Adaptation

  • Qian Sun
  • Rita Chattopadhyay
  • Sethuraman Panchanathan
  • Jieping Ye

Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution but may have plenty of labeled data from multiple related sources with different distributions. The difference in distributions may be in both marginal and conditional probabilities. Most of the existing domain adaptation work focuses on the marginal probability distribution difference between the domains, assuming that the conditional probabilities are similar. However in many real world applications, conditional probability distribution differences are as commonplace as marginal probability differences. In this paper we propose a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data. The weights for minimizing the marginal probability differences are estimated independently, while the weights for minimizing conditional probability differences are computed simultaneously by exploiting the potential interaction among multiple sources. We also provide a theoretical analysis on the generalization performance of the proposed multi-source domain adaptation formulation using the weighted Rademacher complexity measure. Empirical comparisons with existing state-of-the-art domain adaptation methods using three real-world datasets demonstrate the effectiveness of the proposed approach.

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