2,980 research outputs found
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
Shape Parameter Estimation
Performance of machine learning approaches depends strongly on the choice of
misfit penalty, and correct choice of penalty parameters, such as the threshold
of the Huber function. These parameters are typically chosen using expert
knowledge, cross-validation, or black-box optimization, which are time
consuming for large-scale applications. We present a principled, data-driven
approach to simultaneously learn the model pa- rameters and the misfit penalty
parameters. We discuss theoretical properties of these joint inference
problems, and develop algorithms for their solution. We show synthetic examples
of automatic parameter tuning for piecewise linear-quadratic (PLQ) penalties,
and use the approach to develop a self-tuning robust PCA formulation for
background separation.Comment: 20 pages, 10 figure
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
We examine the recovery of block sparse signals and extend the framework in
two important directions; one by exploiting signals' intra-block correlation
and the other by generalizing signals' block structure. We propose two families
of algorithms based on the framework of block sparse Bayesian learning (BSBL).
One family, directly derived from the BSBL framework, requires knowledge of the
block structure. Another family, derived from an expanded BSBL framework, is
based on a weaker assumption on the block structure, and can be used when the
block structure is completely unknown. Using these algorithms we show that
exploiting intra-block correlation is very helpful in improving recovery
performance. These algorithms also shed light on how to modify existing
algorithms or design new ones to exploit such correlation and improve
performance.Comment: Matlab codes can be downloaded at:
https://sites.google.com/site/researchbyzhang/bsbl, or
http://dsp.ucsd.edu/~zhilin/BSBL.htm
Bayesian Persuasion for Algorithmic Recourse
When subjected to automated decision-making, decision subjects may
strategically modify their observable features in ways they believe will
maximize their chances of receiving a favorable decision. In many practical
situations, the underlying assessment rule is deliberately kept secret to avoid
gaming and maintain competitive advantage. The resulting opacity forces the
decision subjects to rely on incomplete information when making strategic
feature modifications. We capture such settings as a game of Bayesian
persuasion, in which the decision maker offers a form of recourse to the
decision subject by providing them with an action recommendation (or signal) to
incentivize them to modify their features in desirable ways. We show that when
using persuasion, the decision maker and decision subject are never worse off
in expectation, while the decision maker can be significantly better off. While
the decision maker's problem of finding the optimal Bayesian
incentive-compatible (BIC) signaling policy takes the form of optimization over
infinitely-many variables, we show that this optimization can be cast as a
linear program over finitely-many regions of the space of possible assessment
rules. While this reformulation simplifies the problem dramatically, solving
the linear program requires reasoning about exponentially-many variables, even
in relatively simple cases. Motivated by this observation, we provide a
polynomial-time approximation scheme that recovers a near-optimal signaling
policy. Finally, our numerical simulations on semi-synthetic data empirically
demonstrate the benefits of using persuasion in the algorithmic recourse
setting.Comment: In the thirty-sixth Conference on Neural Information Processing
Systems (NeurIPS 2022
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