47,274 research outputs found
Recommended from our members
Algorithms for Query-Efficient Active Learning
Recent decades have witnessed great success of machine learning, especially for tasks where large annotated datasets are available for training models. However, in many applications, raw data, such as images, are abundant, but annotations, such as descriptions of images, are scarce. Annotating data requires human effort and can be expensive. Consequently, one of the central problems in machine learning is how to train an accurate model with as few human annotations as possible. Active learning addresses this problem by bringing the annotator to work together with the learner in the learning process. In active learning, a learner can sequentially select examples and ask the annotator for labels, so that it may require fewer annotations if the learning algorithm avoids querying less informative examples.This dissertation focuses on designing provable query-efficient active learning algorithms. The main contributions are as follows. First, we study noise-tolerant active learning in the standard stream-based setting. We propose a computationally efficient algorithm for actively learning homogeneous halfspaces under bounded noise, and prove it achieves nearly optimal label complexity. Second, we theoretically investigate a novel interactive model where the annotator can not only return noisy labels, but also abstain from labeling. We propose an algorithm which utilizes abstention responses, and analyze its statistical consistency and query complexity under different conditions of the noise and abstention rate. Finally, we study how to utilize auxiliary datasets in active learning. We consider a scenario where the learner has access to a logged observational dataset where labeled examples are observed conditioned on a selection policy. We propose algorithms that effectively take advantage of both auxiliary datasets and active learning. We prove that these algorithms are statistically consistent, and achieve a lower label requirement than alternative methods theoretically and empirically
Visual Causal Feature Learning
We provide a rigorous definition of the visual cause of a behavior that is
broadly applicable to the visually driven behavior in humans, animals, neurons,
robots and other perceiving systems. Our framework generalizes standard
accounts of causal learning to settings in which the causal variables need to
be constructed from micro-variables. We prove the Causal Coarsening Theorem,
which allows us to gain causal knowledge from observational data with minimal
experimental effort. The theorem provides a connection to standard inference
techniques in machine learning that identify features of an image that
correlate with, but may not cause, the target behavior. Finally, we propose an
active learning scheme to learn a manipulator function that performs optimal
manipulations on the image to automatically identify the visual cause of a
target behavior. We illustrate our inference and learning algorithms in
experiments based on both synthetic and real data.Comment: Accepted at UAI 201
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
After being collected for patient care, Observational Health Data (OHD) can
further benefit patient well-being by sustaining the development of health
informatics and medical research. Vast potential is unexploited because of the
fiercely private nature of patient-related data and regulations to protect it.
Generative Adversarial Networks (GANs) have recently emerged as a
groundbreaking way to learn generative models that produce realistic synthetic
data. They have revolutionized practices in multiple domains such as
self-driving cars, fraud detection, digital twin simulations in industrial
sectors, and medical imaging.
The digital twin concept could readily apply to modelling and quantifying
disease progression. In addition, GANs posses many capabilities relevant to
common problems in healthcare: lack of data, class imbalance, rare diseases,
and preserving privacy. Unlocking open access to privacy-preserving OHD could
be transformative for scientific research. In the midst of COVID-19, the
healthcare system is facing unprecedented challenges, many of which of are data
related for the reasons stated above.
Considering these facts, publications concerning GAN applied to OHD seemed to
be severely lacking. To uncover the reasons for this slow adoption, we broadly
reviewed the published literature on the subject. Our findings show that the
properties of OHD were initially challenging for the existing GAN algorithms
(unlike medical imaging, for which state-of-the-art model were directly
transferable) and the evaluation synthetic data lacked clear metrics.
We find more publications on the subject than expected, starting slowly in
2017, and since then at an increasing rate. The difficulties of OHD remain, and
we discuss issues relating to evaluation, consistency, benchmarking, data
modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and
glossary, 51 in total. Inclusion of a large number of recent publications and
expansion of the discussion accordingl
Kernelized design of experiments
This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard. Given a database of observations, an algorithm inspired by statistical design of experiments and kernel methods is presented that selects a set of k instances to be chosen in order to maximize the prediction performance of a support vector machine. It is shown that the algorithm significantly outperforms related approaches on a number of real-world datasets. --
Decision table for classifying point sources based on FIRST and 2MASS databases
With the availability of multiwavelength, multiscale and multiepoch
astronomical catalogues, the number of features to describe astronomical
objects has increases. The better features we select to classify objects, the
higher the classification accuracy is. In this paper, we have used data sets of
stars and quasars from near infrared band and radio band. Then best-first
search method was applied to select features. For the data with selected
features, the algorithm of decision table was implemented. The classification
accuracy is more than 95.9%. As a result, the feature selection method improves
the effectiveness and efficiency of the classification method. Moreover the
result shows that decision table is robust and effective for discrimination of
celestial objects and used for preselecting quasar candidates for large survey
projects.Comment: 10 pages. accepted by Advances in Space Researc
- …