Some notes concerning a generalized KMM-type optimization method for density ratio estimation

In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM.Comment: 17 pages, 4 figure

Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts

Training machine learning models from data with weak supervision and dataset shifts is still challenging. Designing algorithms when these two situations arise has not been explored much, and existing algorithms cannot always handle the most complex distributional shifts. We think the biquality data setup is a suitable framework for designing such algorithms. Biquality Learning assumes that two datasets are available at training time: a trusted dataset sampled from the distribution of interest and the untrusted dataset with dataset shifts and weaknesses of supervision (aka distribution shifts). The trusted and untrusted datasets available at training time make designing algorithms dealing with any distribution shifts possible. We propose two methods, one inspired by the label noise literature and another by the covariate shift literature for biquality learning. We experiment with two novel methods to synthetically introduce concept drift and class-conditional shifts in real-world datasets across many of them. We opened some discussions and assessed that developing biquality learning algorithms robust to distributional changes remains an interesting problem for future research

Adaptive Learning Algorithms for Non-stationary Data

With the wide availability of large amounts of data and acute need for extracting useful information from such data, intelligent data analysis has attracted great attention and contributed to solving many practical tasks, ranging from scientific research, industrial process and daily life. In many cases the data evolve over time or change from one domain to another. The non-stationary nature of the data brings a new challenge for many existing learning algorithms, which are based on the stationary assumption. This dissertation addresses three crucial problems towards the effective handling of non-stationary data by investigating systematic methods for sample reweighting. Sample reweighting is a problem that infers sample-dependent weights for a data collection to match another data collection which exhibits distributional difference. It is known as the density-ratio estimation problem and the estimation results can be used in several machine learning tasks. This research proposes a set of methods for distribution matching by developing novel density-ratio methods that incorporate the characters of different non-stationary data analysis tasks. The contributions are summarized below. First, for the domain adaptation of classification problems a novel discriminative density-ratio method is proposed. This approach combines three learning objectives: minimizing generalized risk on the reweighted training data, minimizing class-wise distribution discrepancy and maximizing the separation margin on the test data. To solve the discriminative density-ratio problem, two algorithms are presented on the basis of a block coordinate update optimization scheme. Experiments conducted on different domain adaptation scenarios demonstrate the effectiveness of the proposed algorithms. Second, for detecting novel instances in the test data a locally-adaptive kernel density-ratio method is proposed. While traditional novelty detection algorithms are limited to detect either emerging novel instances which are completely new, or evolving novel instances whose distribution are different from previously-seen ones, the proposed algorithm builds on the success of the idea of using density ratio as a measure of evolving novelty and augments with structural information of each data instance's neighborhood. This makes the estimation of density ratio more reliable, and results in detection of emerging as well as evolving novelties. In addition, the proposed locally-adaptive kernel novelty detection method is applied in the social media analysis and shows favorable performance over other existing approaches. As the time continuity of social media streams, the novelty is usually characterized by the combination of emerging and evolving. One reason is the existence of large common vocabularies between different topics. Another reason is that there are high possibilities of topics being continuously discussed in sequential batch of collections, but showing different level of intensity. Thus, the presented novelty detection algorithm demonstrates its effectiveness in the social media data analysis. Lastly, an auto-tuning method for the non-parametric kernel mean matching estimator is presented. It introduces a new quality measure for evaluating the goodness of distribution matching which reflects the normalized mean square error of estimates. The proposed quality measure does not depend on the learner in the following step and accordingly allows the model selection procedures for importance estimation and prediction model learning to be completely separated