2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
400
2 - Detecting Anomalous Patterns Of Care Using Health
Insurance Claims
Satya Venkata Somanchi, University of Notre Dame, 344 Mendoza
College of Business, Notre Dame, IN, 46556, United States,
, Edward McFowland
In this work, we propose methods for detecting heterogeneous treatment effects
in observational data. We apply these methods to the patient health insurance
claims to improve clinical practice by analyzing patterns across patients and
providing actionable insights. Our goal is to analyze this complex patient care data
in order to identify interesting patterns in patient care that have led to anomalous
health outcomes. Specifically, we detect treatments in the outpatient patient care
that have significantly deviated from the regular treatment process and have
affected health outcomes either negatively or positively. This can further help
improve patient health and reduce health care costs.
3 - FastMemory-efficient Anomaly Detection In Streaming
Heterogenous Graphs
Emaad Ahmed Manzoor, Carnegie Mellon University, Pittsburgh,
PA, 19106, United States,
We present StreamSpot, a method to continuously track anomalous graphs as
they evolve from a stream of edges with node and edge types. We introduce a
graph similarity function that captures both node/edge types and temporal order,
and a constant-space sketch that can be maintained incrementally to approximate
this similarity in constant time. We show that when applied to detect malicious
software from a stream of system call traces of executing processes, StreamSpot
scales to over 10,000 edges/second and demonstrates an average precision of over
95%. Project website:
4 - Sequential Goodness Of Fit Testing With False Discovery
Rate Control
Sangwon (Justin) Hyun, Carnegie Mellon, Pittsburgh, PA,
United States, , Max G’Sell
We consider sequential goodness-of-fit testing for sequential model selection
procedures. This leads to a multiple hypothesis testing setting where the
hypotheses are ordered and one is only permitted to reject an initial contiguous
block, H_1, ..., H_k, of hypotheses. A rejection rule in this setting amounts to a
procedure for choosing the stopping point k, corresponding to a particular
selected model. We will discuss a multiple hypothesis testing procedure for FDR
control in this setting. We will also introduce recent results for goodness-of-fit
testing for clustering and the graphical lasso as an illustration of this approach.
WB16
105A-MCC
Optimal Learning and Optimization via Simulation
Sponsored: Optimization, Optimization Under Uncertainty
Sponsored Session
Chair: Peter Frazier, Cornell University, 410 Thurston Ave, Ithaca, NY,
United States,
1 - Optimal Learning With Discrete Priors
Weidong Han, Princeton University,
We consider an optimal learning problem with a random parameter following a
discrete prior distribution. We formulate the problem into a dynamic program,
and propose several applicable polices. We consider both finite-horizon and
infinite-horizon problems, provide a lower bound on a well-known heuristic
policy in the former case, and prove asymptotic convergence properties of the
proposed policies in the latter case. We also present comparisons against an
optimal learning policy for selected problem instances. Empirical experiments
show that the proposed policies achieve near-optimal performances.
2 - Parallel Knowledge Gradient For Global Optimization Of
Expensive Functions
Jian Wu, Cornell University, Ithaca, NY, 14850, United States,
, Peter Frazier
In this talk, we will introduce parallel knowledge gradient (pKG) algorithm for
batch Bayesian optimization. The method chooses to evaluate the one-step Bayes
optimal batch of points. We demonstrate empirically that the method can find
global minima significantly faster than previous batch Bayesian Optimization
methods on both synthetic functions and tuning hyperparameters of complex
machine learning algorithms. Especially, the method provides most value in noisy
setting.
3 - Secant Tangents-averaged Stochastic Approximation (STAR-SA)
Marie Chau, Virginia Commonwealth University,
We present new theoretical results for Secant Tangents-AveRaged stochastic
approximation (STAR-SA) and extend it to higher dimensions using simultaneous
perturbation stochastic approximation. Under a setting where both direct and
indirect gradients are available, STAR-SA and STAR-SPSA (for the
multidimensional case) combine direct and indirect gradients using a convex
weight. We derive deterministic weights to minimize the MSE of the gradient and
of the estimate, both of which are lower than the classical Robbins-Monro and
Kiefer-Wolfowitz SA methods. We prove convergence, show asymptotic
normality, and investigate their empirical performance against well-known SA
algorithms.
4 - Optimal Learning For Nonlinear Parametric Belief Models With
Continuous Alternatives
Xinyu He, Princeton University, Princeton, NJ, United States,
Warren B Powell
We consider the optimal learning problem for nonlinear parametric belief models,
where our goal is to find the optimal alternative through a series of noisy and
expensive measurements. This problem has been studied when the number of
alternatives is finite. Our work presents an algorithm that extends the optimal
learning framework to the case with continuous alternatives. Experiments show
that our algorithm on the continuous framework exhibits significant performance
improvements, especially in higher dimensions.
WB17
105B-MCC
Distributed Consensus Optimization
Sponsored: Optimization, Nonlinear Programming
Sponsored Session
Chair: Necdet Serhat Aybat, The Pennsylvania State University,
310 Leonhard Building, University Park, PA, 16802, United States,
1 - DQM: Decentralized Quadratically Approximated Alternating
Direction Method Of Multipliers
Aryan Mokhtari, University of Pennsylvania, 200 South 33rd
Street, Room 203 Moore Building, Philadelphia, PA, 19104,
United States, , Wei Shi, Qing Ling,
Alejandro Ribeiro
The decentralized alternating method of multipliers (DADMM) is a well-
established iterative method for solving consensus optimization problems;
however, implementation of DADMM requires solving an optimization
subproblem at each iteration for each node which can be computationally costly.
We propose a decentralized quadratic approximation of ADMM (DQM) that
reduces the computational complexity of DADMM by minimizing a quadratic
approximation of the objective function. We show that DQM converges to the
optimal argument at a linear rate which is identical to the convergence rate of
DADMM. Moreover, as time passes the coefficient of linear convergence for DQM
approaches the one for DADMM.
2 - A Geometrically Convergent Method For Distributed Optimization
Over Time-varying And Directed Graphs
Wei Shi, Postdoctoral Research Associate, Coordinated Science
Laboratory, University of Illinois at Urbana-Champaign, Urbana,
IL, 61801, United States, Angelia Nedich,
Alex Olshevsky
In this presentation, we introduce a class of first-order algorithms, referred to as
DIGing and its variants, for distributed optimization over time-varying and/or
directed graphs. The algorithms employ fixed step-sizes and, yet, drives all the
agents’ iterates to a global and consensual minimizer. Under the strong convexity
assumption, we show that the algorithms all converge at some explicit global R-
linear (geometric) rates as long as the step-sizes are no greater than some explicit
bounds. We also show that the rate for DIGing scales polynomially in the number
of agents. Numerical experiments demonstrate the efficacy of the introduced
algorithms and validate our theoretical findings.
3 - Distributed Admm-like Methods For Cooperative Multi-agent
Optimization Over Conic Constraints
Erfan Yazdandoost Hamedani, Penn State University, Department
of Industrial Engineering, 310 Leonhard Bldg. University Park,
State College, PA, United States,
Necdet Serhat Aybat
We consider the sharing problem over an undirected (dynamic) network of
agents, where only those agents connected by an edge can directly communicate.
The objective is to minimize the sum of agent-specific composite convex functions
subject to a conic constraint coupling all agents’ local decisions. An ADMM-like
primal-dual distributed algorithm is proposed. We examine the convergence rate
in terms of suboptimality, and infeasibility; study the effect of underlying network
topology on the rates; and show how to extend this method to handle
communication networks with time-varying topology.
WB16