54,416 research outputs found
Causally Regularized Learning with Agnostic Data Selection Bias
Most of previous machine learning algorithms are proposed based on the i.i.d.
hypothesis. However, this ideal assumption is often violated in real
applications, where selection bias may arise between training and testing
process. Moreover, in many scenarios, the testing data is not even available
during the training process, which makes the traditional methods like transfer
learning infeasible due to their need on prior of test distribution. Therefore,
how to address the agnostic selection bias for robust model learning is of
paramount importance for both academic research and real applications. In this
paper, under the assumption that causal relationships among variables are
robust across domains, we incorporate causal technique into predictive modeling
and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm
by jointly optimize global confounder balancing and weighted logistic
regression. Global confounder balancing helps to identify causal features,
whose causal effect on outcome are stable across domains, then performing
logistic regression on those causal features constructs a robust predictive
model against the agnostic bias. To validate the effectiveness of our CRLR
algorithm, we conduct comprehensive experiments on both synthetic and real
world datasets. Experimental results clearly demonstrate that our CRLR
algorithm outperforms the state-of-the-art methods, and the interpretability of
our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18
Understanding and Diagnosing Visual Tracking Systems
Several benchmark datasets for visual tracking research have been proposed in
recent years. Despite their usefulness, whether they are sufficient for
understanding and diagnosing the strengths and weaknesses of different trackers
remains questionable. To address this issue, we propose a framework by breaking
a tracker down into five constituent parts, namely, motion model, feature
extractor, observation model, model updater, and ensemble post-processor. We
then conduct ablative experiments on each component to study how it affects the
overall result. Surprisingly, our findings are discrepant with some common
beliefs in the visual tracking research community. We find that the feature
extractor plays the most important role in a tracker. On the other hand,
although the observation model is the focus of many studies, we find that it
often brings no significant improvement. Moreover, the motion model and model
updater contain many details that could affect the result. Also, the ensemble
post-processor can improve the result substantially when the constituent
trackers have high diversity. Based on our findings, we put together some very
elementary building blocks to give a basic tracker which is competitive in
performance to the state-of-the-art trackers. We believe our framework can
provide a solid baseline when conducting controlled experiments for visual
tracking research
Estimating Parameters in Autoregressive Models with Asymmetric Innovations
Tiku et al (1999) considered the estimation in a regression model with autocorrelated error in which the underlying distribution be a shift-scaled Student’s t distribution, developed the modified maximum likelihood (MML) estimators of the parameters and showed that the proposed estimators had closed forms and were remarkably efficient and robust. In this paper, we extend the results to the case, where the underlying distribution is a generalized logistic distribution. The generalized logistic distribution family represents very wide skew distributions ranging from highly right skewed to highly left skewed. Analogously, we develop the MML estimators since the ML (maximum likelihood) estimators are intractable for the generalized logistic data. We then study the asymptotic properties of the proposed estimators and conduct simulation to the study.
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