2015 Informs Annual Meeting

MC21

INFORMS Philadelphia – 2015

3 - Towards Multi-resource Fairness in Big Data Systems Zhenhua Liu, Assistant Professor, Stony Brook University, Stony Brook, NY, 11794, United States of America, zhenhua.liu@stonybrook.edu Big data systems nowadays involve multiple resources such as CPU, memory, network during multiple stages. On the other hand, these systems are usually shared among multiple tenants with different demand characteristics. How to optimally align these two complexities while maintaining fairness among tenants has significant theoretical challenges, while generates great practical value. In this talk, I will briefly introduce our recent progress along this.

discuss how to extend many of the discrete results to the continuous setting using lifts of graphs. 4 - Factor Graphs, Kramers-Wannier Duality, and the Sum-product Algorithm Ali Al-Bashabsheh, Postdoc, The Chinese University of Hong Kong, Hong Kong, Hong Kong - PRC, entropyali@gmail.com, Pascal O. Vontobel A key object associated with a graphical model is its partition function. Although the partition function is often intractable, it can be estimated (e.g., via the sum- product algorithm) or analyzed (e.g., via factor graph transforms). An example of the latter, and also the main focus of this talk, is the analysis of 2D-Ising models via Kramers—Wannier duality. At various places we will point out connections to optimization problems. MC23 23-Franklin 13, Marriott Optimal Control of Stochastic Systems Sponsor: Applied Probability Sponsored Session Chair: Jiheng Zhang, HKUST, Clear Water Bay, Hong Kong, Hong Kong - PRC, j.zhang@ust.hk 1 - Distributionally Robust Inventory Control when Demand is a Martingale Linwei Xin, Assistant Professor, University of Illinois at Urbana-Champaign, 104 S. Mathews Ave., Urbana, IL, 61801, United States of America, lxin@illinois.edu, David Goldberg Independence of random demands across different periods is typically assumed in multi-period inventory models. In this talk, we consider a distributionally robust model in which the sequence of demands must take the form of a martingale with given mean and support. We explicitly compute the optimal policy and value, and shed light on the interplay between the optimal policy and worst-case martingale. We also compare to the analogous setting in which demand is independent across periods. 2 - Join the Shortest Queue with Customer Abandonment Ping Cao, University of Science and Technology of China, Room 707A, School of Management, Hefei, China, pcao@ustc.edu.cn, Junfei Huang We consider an overloaded queueing system with many servers and customer abandonment under the join-the-shortest-queue policy. Diffusion approximations for system performances are established. The approximation expressions depend on the traffic intensity: in some cases a one-dimensional Ornstein-Uhlenbeck process is enough while in other cases a two-dimensional process is necessary. We Hong Kong - PRC, j.zhang@ust.hk, Rachel Zhang, Hailun Zhang We study joint replenishment and clearance of perishable products when the demand rate is large. We proposes two policies based on fluid and diffusion approximations, respectively. The fluid based policy can achieve asymptotic optimality with the gap explicitly computed. The diffusion based policy can significantly improve the gap when the initial inventory is small. When the initial inventory is large, we prove that depletion-once is enough to achieve asymptotic optimality. also compare the results with that of the one-global-queue system. 3 - Asymptotic Optimal Control of Perishable Inventory Jiheng Zhang, HKUST, Clear Water Bay, Hong Kong,

MC21 21-Franklin 11, Marriott Pierskalla Award Finalists Sponsor: Health Applications Sponsored Session

Chair: Mohsen Bayati, Assistant Professor, Stanford Graduate School of Business, 655 Knight Way, Stanford, CA, United States of America, bayati@stanford.edu Co-Chair: Soo-Haeng Cho, Associate Professor, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, United States of America, soohaeng@andrew.cmu.edu 1 - Pierskalla Award Finalists Mohsen Bayati, Assistant Professor, Stanford Graduate School of The Health Applications Society of INFORMS sponsors an annual competition for the Pierskalla Award, which recognizes research excellence in the field of health care management science. The award is named after Dr. William Pierskalla to recognize his contribution and dedication to improving health services delivery through operations research. The Pierskalla award information can be found on the website at: https://www.informs.org/Community/HAS/Pierskalla-Award MC22 22-Franklin 12, Marriott Message Passing for Inference Sponsor: Applied Probability Sponsored Session Chair: Jinwoo Shin, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, Republic of, jinwoos@kaist.ac.kr 1 - How Hard is Inference for Structured Prediction? David Sontag, Assistant Professor, NYU, 715 Broadway, 12th Floor, Room 1204, New York, NY, 10003, United States of America, dsontag@cs.nyu.edu Structured prediction tasks in machine learning involve the simultaneous prediction of multiple labels. This is typically done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise terms, each depending on two specific labels. Although marginal and MAP inference for these models are NP-hard in the worst-case, approximate inference algorithms are often remarkably successful. In this talk, we develop a theoretical framework to explain why. 2 - Tractable Graphical Modeling and the Bethe Approximation Tony Jebara, Professor, Columbia University, 500 West 120 St., We consider three NP-hard graphical modeling problems. For maximum a posteriori inference, we identify the limits of tractability via perfect graph theory. For marginal inference, we provide efficient solutions using Bethe free energy approximations and discretization. For learning, we combine Bethe with a Frank- Wolfe algorithm to avoid intractable partition functions. Applications include link prediction, social influence estimation, computer vision, financial networks and power networks. 3 - Lifts of Graphs and Approximate Inference Nicholas Ruozzi, Assistant Professor, UT Dallas, 2601 N. Floyd Rd. MS EC31, Richardson, TX, 75080, United States of America, nicholas.ruozzi@utdallas.edu The approximate maximum a posteriori inference problem (MAP) for graphical models over finite state spaces is an NP-hard problem in general. As a result, approximate MAP inference techniques based on convex relaxations are often employed in practice. These convex relaxations are relatively well-understood in the discrete case but many open questions remain in the continuous setting. I will Business, 655 Knight Way, Stanford, CA, United States of America, bayati@stanford.edu, Soo-Haeng Cho, Joel Goh Room 450, Mail Code 0401, New York, NY, 10027, United States of America, jebara@cs.columbia.edu

MC24 24-Room 401, Marriott Network Modeling and Analysis Sponsor: Artificial Intelligence Sponsored Session

Chair: Junming Yin, University of Arizona, Department of MIS, Tucson, AZ, 85721, United States of America, junmingy@email.arizona.edu 1 - Analysis of Network Experiments with Nonnegative Treatment Effects David Choi, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, United States of America, davidch@andrew.cmu.edu Randomized experiments in network settings are potentially useful for understanding the effects of peer influence and other social mechanisms. However, the analysis of experiments is an open problem when the individuals in the experiment are assumed to be able to influence each other’s decisions. We propose a new method that requires much weaker assumptions than existing methods, which often impose stylized models of individual behavior that may not be valid in practice.

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