52,337 research outputs found
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulationâoptimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Quantum Processors and Controllers
In this paper is presented an abstract theory of quantum processors and
controllers, special kind of quantum computational network defined on a
composite quantum system with two parts: the controlling and controlled
subsystems. Such approach formally differs from consideration of quantum
control as some external influence on a system using some set of Hamiltonians
or quantum gates. The model of programmed quantum controllers discussed in
present paper is based on theory of universal deterministic quantum processors
(programmable gate arrays). Such quantum devices may simulate arbitrary
evolution of quantum system and so demonstrate an example of universal quantum
control.
Keywords: Quantum, Computer, Control, Processor, UniversalComment: LaTeXe, 7 pp, 2 col, v3: revised and extended (+50%), PhysCon0
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data
We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending
publication.) Last conference version: submitted March 30, 2012 to UAI 2012.
First conference version: entitled, Learning Influence Games, initially
submitted on June 1, 2010 to NIPS 201
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
The nascent field of fair machine learning aims to ensure that decisions
guided by algorithms are equitable. Over the last several years, three formal
definitions of fairness have gained prominence: (1) anti-classification,
meaning that protected attributes---like race, gender, and their proxies---are
not explicitly used to make decisions; (2) classification parity, meaning that
common measures of predictive performance (e.g., false positive and false
negative rates) are equal across groups defined by the protected attributes;
and (3) calibration, meaning that conditional on risk estimates, outcomes are
independent of protected attributes. Here we show that all three of these
fairness definitions suffer from significant statistical limitations. Requiring
anti-classification or classification parity can, perversely, harm the very
groups they were designed to protect; and calibration, though generally
desirable, provides little guarantee that decisions are equitable. In contrast
to these formal fairness criteria, we argue that it is often preferable to
treat similarly risky people similarly, based on the most statistically
accurate estimates of risk that one can produce. Such a strategy, while not
universally applicable, often aligns well with policy objectives; notably, this
strategy will typically violate both anti-classification and classification
parity. In practice, it requires significant effort to construct suitable risk
estimates. One must carefully define and measure the targets of prediction to
avoid retrenching biases in the data. But, importantly, one cannot generally
address these difficulties by requiring that algorithms satisfy popular
mathematical formalizations of fairness. By highlighting these challenges in
the foundation of fair machine learning, we hope to help researchers and
practitioners productively advance the area
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