2,801 research outputs found
Spatial-temporal data mining procedure: LASR
This paper is concerned with the statistical development of our
spatial-temporal data mining procedure, LASR (pronounced ``laser''). LASR is
the abbreviation for Longitudinal Analysis with Self-Registration of
large--small- data. It was motivated by a study of ``Neuromuscular
Electrical Stimulation'' experiments, where the data are noisy and
heterogeneous, might not align from one session to another, and involve a large
number of multiple comparisons. The three main components of LASR are: (1) data
segmentation for separating heterogeneous data and for distinguishing outliers,
(2) automatic approaches for spatial and temporal data registration, and (3)
statistical smoothing mapping for identifying ``activated'' regions based on
false-discovery-rate controlled -maps and movies. Each of the components is
of interest in its own right. As a statistical ensemble, the idea of LASR is
applicable to other types of spatial-temporal data sets beyond those from the
NMES experiments.Comment: Published at http://dx.doi.org/10.1214/074921706000000707 in the IMS
Lecture Notes--Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
On Similarities between Inference in Game Theory and Machine Learning
In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
The soft Dice loss (SDL) has taken a pivotal role in many automated
segmentation pipelines in the medical imaging community. Over the last years,
some reasons behind its superior functioning have been uncovered and further
optimizations have been explored. However, there is currently no implementation
that supports its direct use in settings with soft labels. Hence, a synergy
between the use of SDL and research leveraging the use of soft labels, also in
the context of model calibration, is still missing. In this work, we introduce
Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a
standard setting with hard labels, but (ii) can be used in settings with soft
labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm
the potential synergy of DMLs with soft labels (e.g. averaging, label
smoothing, and knowledge distillation) over hard labels (e.g. majority voting
and random selection). As a result, we obtain superior Dice scores and model
calibration, which supports the wider adoption of DMLs in practice. Code is
available at
\href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to MICCAI2023. Code is available at
https://github.com/zifuwanggg/JDTLosse
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