Match and Reweight Strategy for Generalized Target Shift

We address the problem of unsupervised domain adaptation under the setting of generalized target shift (both class-conditional and label shifts occur). We show that in that setting, for good generalization, it is necessary to learn with similar source and target label distributions and to match the class-conditional probabilities. For this purpose, we propose an estimation of target label proportion by blending mixture estimation and optimal transport. This estimation comes with theoretical guarantees of correctness. Based on the estimation, we learn a model by minimizing a importance weighted loss and a Wasserstein distance between weighted marginals. We prove that this minimization allows to match class-conditionals given mild assumptions on their geometry. Our experimental results show that our method performs better on average than competitors accross a range domain adaptation problems including digits,VisDA and Office

On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost performance of the unweighted version. In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. The analysis immediately provides us explanations for understanding (1) quantitative effects of the class-wise reweighting mechanism: deterministic effectiveness for binary classification using logistic loss yet indeterministic for multi-class classification using softmax loss; (2) disadvantage of logistic loss for single-label multi-class classification via one-vs.-all approach, which is due to the averaging effect on predicted probabilities for the negative class (e.g., non-target classes) in the learning process. With the disadvantage and advantage of logistic loss disentangled, we thereafter propose a novel reweighted logistic loss for multi-class classification. Our simple yet effective formulation improves ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all) and turned out to be competitive with softmax loss. We evaluate our method on several benchmark datasets to demonstrate its effectiveness.Comment: AAAI2020. Previously this appeared as arXiv:1906.04026v2, which was submitted as a replacement by acciden

Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two coupled projections that project the source domain and target domain data into low dimensional subspaces where the geometrical shift and distribution shift are reduced simultaneously. The objective function can be solved efficiently in a closed form. Extensive experiments have verified that the proposed method significantly outperforms several state-of-the-art domain adaptation methods on a synthetic dataset and three different real world cross-domain visual recognition tasks

Investing in Schooling in Chile: The Role of Information about Financial Aid for Higher Education

Recent economic research shows that imperfect information about Mincer returns to education (in developing countries) or about financial aid (in the US) may undermine investments in schooling and exacerbate inequalities in access to education. We extend this literature by presenting the results of an experiment that provided children and a subset of their parents with specific information about financial aid for higher education, and measured the impact on effort in primary school. We developed a DVD information program and randomly assigned a sample of Chilean 8th graders in poor urban schools to information treatment and control groups. Half of the treatment group watched the DVD at school (Student group) and the other half received a copy of the program to watch at home (Family group). Using survey and matched administrative data to measure outcomes three to six months post-intervention, we show that knowledge of financial aid sources improves in treated schools and school-reported absenteeism falls by 14%. These responses appear to be driven by students with higher baseline grades; yet we find no significant effects on 8th Grade scores or 9th Grade enrolment for any students. While parents in the Family treatment group score significantly higher on tests of information related to DVD content, watching the DVD at home is no more effective at changing effort than watching at school, at least for high ability students likely to select in to watching the DVD. Our results suggest that Chile falls somewhere between developing and developed countries: exposure to information about financial aid for post-secondary schooling significantly affects student knowledge and absenteeism, but is insufficient for improving other educational outcomes.finaicial aid, education, Chile, imperfect information, behavior, education investment