170,387 research outputs found
Adaptive Sequential Optimization with Applications to Machine Learning
A framework is introduced for solving a sequence of slowly changing
optimization problems, including those arising in regression and classification
applications, using optimization algorithms such as stochastic gradient descent
(SGD). The optimization problems change slowly in the sense that the minimizers
change at either a fixed or bounded rate. A method based on estimates of the
change in the minimizers and properties of the optimization algorithm is
introduced for adaptively selecting the number of samples needed from the
distributions underlying each problem in order to ensure that the excess risk,
i.e., the expected gap between the loss achieved by the approximate minimizer
produced by the optimization algorithm and the exact minimizer, does not exceed
a target level. Experiments with synthetic and real data are used to confirm
that this approach performs well.Comment: submitted to ICASSP 2016, extended versio
Addictive links: The motivational value of adaptive link annotation
Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work
Adaptive Sensing for Estimation of Structured Sparse Signals
In many practical settings one can sequentially and adaptively guide the
collection of future data, based on information extracted from data collected
previously. These sequential data collection procedures are known by different
names, such as sequential experimental design, active learning or adaptive
sensing/sampling. The intricate relation between data analysis and acquisition
in adaptive sensing paradigms can be extremely powerful, and often allows for
reliable signal estimation and detection in situations where non-adaptive
sensing would fail dramatically.
In this work we investigate the problem of estimating the support of a
structured sparse signal from coordinate-wise observations under the adaptive
sensing paradigm. We present a general procedure for support set estimation
that is optimal in a variety of cases and shows that through the use of
adaptive sensing one can: (i) mitigate the effect of observation noise when
compared to non-adaptive sensing and, (ii) capitalize on structural information
to a much larger extent than possible with non-adaptive sensing. In addition to
a general procedure to perform adaptive sensing in structured settings we
present both performance upper bounds, and corresponding lower bounds for both
sensing paradigms
- …