2,250 research outputs found
Fast Cross-Validation via Sequential Testing
With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the best parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping promising candidates
as long as possible, the method speeds up the computation while preserving the
capability of the full cross-validation. Theoretical considerations underline
the statistical power of our procedure. The experimental evaluation shows that
our method reduces the computation time by a factor of up to 120 compared to a
full cross-validation with a negligible impact on the accuracy
Meta Clustering for Collaborative Learning
An emerging number of learning scenarios involve a set of learners/analysts
each equipped with a unique dataset and algorithm, who may collaborate with
each other to enhance their learning performance. From the perspective of a
particular learner, a careless collaboration with task-irrelevant other
learners is likely to incur modeling error. A crucial problem is to search for
the most appropriate collaborators so that their data and modeling resources
can be effectively leveraged. Motivated by this, we propose to study the
problem of `meta clustering', where the goal is to identify subsets of relevant
learners whose collaboration will improve the performance of each individual
learner. In particular, we study the scenario where each learner is performing
a supervised regression, and the meta clustering aims to categorize the
underlying supervised relations (between responses and predictors) instead of
the raw data. We propose a general method named as Select-Exchange-Cluster
(SEC) for performing such a clustering. Our method is computationally efficient
as it does not require each learner to exchange their raw data. We prove that
the SEC method can accurately cluster the learners into appropriate
collaboration sets according to their underlying regression functions.
Synthetic and real data examples show the desired performance and wide
applicability of SEC to a variety of learning tasks
Capturing Evolution Genes for Time Series Data
The modeling of time series is becoming increasingly critical in a wide
variety of applications. Overall, data evolves by following different patterns,
which are generally caused by different user behaviors. Given a time series, we
define the evolution gene to capture the latent user behaviors and to describe
how the behaviors lead to the generation of time series. In particular, we
propose a uniform framework that recognizes different evolution genes of
segments by learning a classifier, and adopt an adversarial generator to
implement the evolution gene by estimating the segments' distribution.
Experimental results based on a synthetic dataset and five real-world datasets
show that our approach can not only achieve a good prediction results (e.g.,
averagely +10.56% in terms of F1), but is also able to provide explanations of
the results.Comment: a preprint version. arXiv admin note: text overlap with
arXiv:1703.10155 by other author
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Machine learning models will often fail when deployed in an environment with
a data distribution that is different than the training distribution. When
multiple environments are available during training, many methods exist that
learn representations which are invariant across the different distributions,
with the hope that these representations will be transportable to unseen
domains. In this work, we present a nonparametric strategy for learning
invariant representations based on the recently-proposed Nadaraya-Watson (NW)
head. The NW head makes a prediction by comparing the learned representations
of the query to the elements of a support set that consists of labeled data. We
demonstrate that by manipulating the support set, one can encode different
causal assumptions. In particular, restricting the support set to a single
environment encourages the model to learn invariant features that do not depend
on the environment. We present a causally-motivated setup for our modeling and
training strategy and validate on three challenging real-world domain
generalization tasks in computer vision.Comment: Accepted to NeurIPS 202
Tune in to your emotions: a robust personalized affective music player
The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listenersâ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application
Meta Learning for Causal Direction
The inaccessibility of controlled randomized trials due to inherent
constraints in many fields of science has been a fundamental issue in causal
inference. In this paper, we focus on distinguishing the cause from effect in
the bivariate setting under limited observational data. Based on recent
developments in meta learning as well as in causal inference, we introduce a
novel generative model that allows distinguishing cause and effect in the small
data setting. Using a learnt task variable that contains distributional
information of each dataset, we propose an end-to-end algorithm that makes use
of similar training datasets at test time. We demonstrate our method on various
synthetic as well as real-world data and show that it is able to maintain high
accuracy in detecting directions across varying dataset sizes
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