73,659 research outputs found
Structural Drift: The Population Dynamics of Sequential Learning
We introduce a theory of sequential causal inference in which learners in a
chain estimate a structural model from their upstream teacher and then pass
samples from the model to their downstream student. It extends the population
dynamics of genetic drift, recasting Kimura's selectively neutral theory as a
special case of a generalized drift process using structured populations with
memory. We examine the diffusion and fixation properties of several drift
processes and propose applications to learning, inference, and evolution. We
also demonstrate how the organization of drift process space controls fidelity,
facilitates innovations, and leads to information loss in sequential learning
with and without memory.Comment: 15 pages, 9 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/sdrift.ht
Distributed Robust Learning
We propose a framework for distributed robust statistical learning on {\em
big contaminated data}. The Distributed Robust Learning (DRL) framework can
reduce the computational time of traditional robust learning methods by several
orders of magnitude. We analyze the robustness property of DRL, showing that
DRL not only preserves the robustness of the base robust learning method, but
also tolerates contaminations on a constant fraction of results from computing
nodes (node failures). More precisely, even in presence of the most adversarial
outlier distribution over computing nodes, DRL still achieves a breakdown point
of at least , where is the break down point of
corresponding centralized algorithm. This is in stark contrast with naive
division-and-averaging implementation, which may reduce the breakdown point by
a factor of when computing nodes are used. We then specialize the
DRL framework for two concrete cases: distributed robust principal component
analysis and distributed robust regression. We demonstrate the efficiency and
the robustness advantages of DRL through comprehensive simulations and
predicting image tags on a large-scale image set.Comment: 18 pages, 2 figure
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine
communications, in which users may start or stop transmitting at any time in an
asynchronous manner. Thus, the number of users is an unknown and time-varying
parameter that needs to be accurately estimated in order to properly recover
the symbols transmitted by all users in the system. In this paper, we address
the problem of joint channel parameter and data estimation in a multiuser
communication channel in which the number of transmitters is not known. For
that purpose, we develop the infinite factorial finite state machine model, a
Bayesian nonparametric model based on the Markov Indian buffet that allows for
an unbounded number of transmitters with arbitrary channel length. We propose
an inference algorithm that makes use of slice sampling and particle Gibbs with
ancestor sampling. Our approach is fully blind as it does not require a prior
channel estimation step, prior knowledge of the number of transmitters, or any
signaling information. Our experimental results, loosely based on the LTE
random access channel, show that the proposed approach can effectively recover
the data-generating process for a wide range of scenarios, with varying number
of transmitters, number of receivers, constellation order, channel length, and
signal-to-noise ratio.Comment: 15 pages, 15 figure
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