3,496 research outputs found
Recovering Markov Models from Closed-Loop Data
Situations in which recommender systems are used to augument decision making
are becoming prevalent in many application domains. Almost always, these
prediction tools (recommenders) are created with a view to affecting
behavioural change. Clearly, successful applications actuating behavioural
change, affect the original model underpinning the predictor, leading to an
inconsistency. This feedback loop is often not considered in standard so-called
Big Data learning techniques which rely upon machine learning/statistical
learning machinery. The objective of this paper is to develop tools that
recover unbiased user models in the presence of recommenders. More
specifically, we assume that we observe a time series which is a trajectory of
a Markov chain modulated by another Markov chain , i.e. the
transition matrix of is unknown and depends on the current state of
. The transition matrix of the latter is also unknown. In other words, at
each time instant, selects a transition matrix for within a given
set which consists of known and unknown matrices. The state of , in turn,
depends on the current state of thus introducing a feedback loop. We
propose an Expectation-Maximization (EM) type algorithm, which estimates the
transition matrices of and . Experimental results are given to
demonstrate the efficacy of the approach
An Adaptive Markov Random Field for Structured Compressive Sensing
Exploiting intrinsic structures in sparse signals underpins the recent
progress in compressive sensing (CS). The key for exploiting such structures is
to achieve two desirable properties: generality (\ie, the ability to fit a wide
range of signals with diverse structures) and adaptability (\ie, being adaptive
to a specific signal). Most existing approaches, however, often only achieve
one of these two properties. In this study, we propose a novel adaptive Markov
random field sparsity prior for CS, which not only is able to capture a broad
range of sparsity structures, but also can adapt to each sparse signal through
refining the parameters of the sparsity prior with respect to the compressed
measurements. To maximize the adaptability, we also propose a new sparse signal
estimation where the sparse signals, support, noise and signal parameter
estimation are unified into a variational optimization problem, which can be
effectively solved with an alternative minimization scheme. Extensive
experiments on three real-world datasets demonstrate the effectiveness of the
proposed method in recovery accuracy, noise tolerance, and runtime.Comment: 13 pages, submitted to IEEE Transactions on Image Processin
Neural Network Based Nonlinear Weighted Finite Automata
Weighted finite automata (WFA) can expressively model functions defined over
strings but are inherently linear models. Given the recent successes of
nonlinear models in machine learning, it is natural to wonder whether
ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we
propose a novel model of neural network based nonlinearWFA model (NL-WFA) along
with a learning algorithm. Our learning algorithm is inspired by the spectral
learning algorithm for WFAand relies on a nonlinear decomposition of the
so-called Hankel matrix, by means of an auto-encoder network. The expressive
power of NL-WFA and the proposed learning algorithm are assessed on both
synthetic and real-world data, showing that NL-WFA can lead to smaller model
sizes and infer complex grammatical structures from data.Comment: AISTATS 201
Stochastic Stability of Event-triggered Anytime Control
We investigate control of a non-linear process when communication and
processing capabilities are limited. The sensor communicates with a controller
node through an erasure channel which introduces i.i.d. packet dropouts.
Processor availability for control is random and, at times, insufficient to
calculate plant inputs. To make efficient use of communication and processing
resources, the sensor only transmits when the plant state lies outside a
bounded target set. Control calculations are triggered by the received data. If
a plant state measurement is successfully received and while the processor is
available for control, the algorithm recursively calculates a sequence of
tentative plant inputs, which are stored in a buffer for potential future use.
This safeguards for time-steps when the processor is unavailable for control.
We derive sufficient conditions on system parameters for stochastic stability
of the closed loop and illustrate performance gains through numerical studies.Comment: IEEE Transactions on Automatic Control, under revie
Recovering Robustness in Model-Free Reinforcement learning
Reinforcement learning (RL) is used to directly design a control policy using
data collected from the system. This paper considers the robustness of
controllers trained via model-free RL. The discussion focuses on the standard
model-based linear quadratic Gaussian (LQG) problem as a special instance of
RL. A simple example, originally formulated for LQG problems, is used to
demonstrate that RL with partial observations can lead to poor robustness
margins. It is proposed to recover robustness by introducing random
perturbations at the system input during the RL training. The perturbation
magnitude can be used to trade off performance for robustness. Two simple
examples are presented to demonstrate the proposed method for enhancing
robustness during RL training.Comment: Github Code Repository: https://github.com/kumaa001/RLRobustness
(Note : The files have been named to match with the section names and number.
The comments in the code explains the procedure step by step. The data from
the .mat file could be pulled into the work-space to avoid the need for
complete code execution
Spatial Disaggregation of Agricultural Production Data
In this paper we develop a dynamic data-consistent way for estimating agricultural land use choices at a disaggregate level (district-level), using more aggregate data (regional-level). The disaggregation procedure requires two steps. The first step consists in specifying and estimating a dynamic model of land use at the regional level. In the second step, we disaggregate outcomes of the aggregate model using maximum entropy (ME). The ME disaggregation procedure is applied to a sample of California data. The sample includes 6 districts located in Central Valley and 8 possible crops, namely: Alfalfa, Cotton, Field, Grain, Melons, Tomatoes, Vegetables and Subtropical. The disaggregation procedure enables the recovery of land use at the district-level with an out-sample prediction error of 16%. This result shows that the micro behavior, inferred from aggregate data with our disaggregation approach, seems to be consistent with observed behavior.Disaggregation, Bayesian method, Maximum entropy, Land use, Production Economics, C11, C44, Q12,
Sequential Source Coding for Stochastic Systems Subject to Finite Rate Constraints
In this paper, we revisit the sequential source coding framework to analyze
fundamental performance limitations of discrete-time stochastic control systems
subject to feedback data-rate constraints in finite-time horizon. The basis of
our results is a new characterization of the lower bound on the minimum
total-rate achieved by sequential codes subject to a total (across time)
distortion constraint and a computational algorithm that allocates optimally
the rate-distortion for any fixed finite-time horizon. This characterization
facilitates the derivation of analytical, non-asymptotic, and
finite-dimensional lower and upper bounds in two control-related scenarios. (a)
A parallel time-varying Gauss-Markov process with identically distributed
spatial components that is quantized and transmitted through a noiseless
channel to a minimum mean-squared error (MMSE) decoder. (b) A time-varying
quantized LQG closed-loop control system, with identically distributed spatial
components and with a random data-rate allocation. Our non-asymptotic lower
bound on the quantized LQG control problem, reveals the absolute minimum
data-rates for (mean square) stability of our time-varying plant for any fixed
finite time horizon. We supplement our framework with illustrative simulation
experiments.Comment: 40 pages, 6 figure
Anchored Discrete Factor Analysis
We present a semi-supervised learning algorithm for learning discrete factor
analysis models with arbitrary structure on the latent variables. Our algorithm
assumes that every latent variable has an "anchor", an observed variable with
only that latent variable as its parent. Given such anchors, we show that it is
possible to consistently recover moments of the latent variables and use these
moments to learn complete models. We also introduce a new technique for
improving the robustness of method-of-moment algorithms by optimizing over the
marginal polytope or its relaxations. We evaluate our algorithm using two
real-world tasks, tag prediction on questions from the Stack Overflow website
and medical diagnosis in an emergency department
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path
choice behavior of network users. This problem has been extensively studied in
transportation science, where it is known as the route choice problem. In this
literature, individuals' choice of paths are typically predicted using discrete
choice models. This article is a tutorial on a specific category of discrete
choice models called recursive, and it makes three main contributions: First,
for the purpose of assisting future research on route choice, we provide a
comprehensive background on the problem, linking it to different fields
including inverse optimization and inverse reinforcement learning. Second, we
formally introduce the problem and the recursive modeling idea along with an
overview of existing models, their properties and applications. Third, we
extensively analyze illustrative examples from different angles so that a
novice reader can gain intuition on the problem and the advantages provided by
recursive models in comparison to path-based ones
A Monte Carlo method for critical systems in infinite volume: the planar Ising model
In this paper we propose a Monte Carlo method for generating finite-domain
marginals of critical distributions of statistical models in infinite volume.
The algorithm corrects the problem of the long-range effects of boundaries
associated to generating critical distributions on finite lattices. It uses the
advantage of scale invariance combined with ideas of the renormalization group
in order to construct a type of "holographic" boundary condition that encodes
the presence of an infinite volume beyond it. We check the quality of the
distribution obtained in the case of the planar Ising model by comparing
various observables with their infinite-plane prediction. We accurately
reproduce planar two-, three- and four-point functions of spin and energy
operators. We also define a lattice stress-energy tensor, and numerically
obtain the associated conformal Ward identities and the Ising central charge.Comment: 43 pages, 21 figure
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