Research

Journal Articles

[J16] W. Huang and W. B. Haskell. Stochastic approximation for risk-aware Markov decision processes. Conditionally accepted in IEEE Transactions on Automatic Control. [arXiv]

[J15] W. B. Haskell, R. Jain, H. Sharma, and P. Yu. Randomized function fitting for empirical dynamic programming. Conditionally accepted in IEEE Transactions on Automatic Control. [arXiv]

[J14] W. B. Haskell and A. Toriello. Modeling stochastic dominance as infinite-dimensional constraint systems via Strassen’s theorem. Journal of Optimisation Theory and Applications, Vol. 178, No. 3, Pages 726 - 742, 2018. [Link]

[J13] S. Zhao, W. B. Haskell, and M. Cardin. Decision Rule based Method for Flexible Multi-Facility Capacity Expansion Problem. IISE Transactions, Vol. 50, No. 7, Pages 553 - 569, 2018. [Link]

[J12] R. Zhao, W. B. Haskell, and V. Tan. Stochastic LBFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies. IEEE Transactions on Signal Processing, Vol. 66, No. 5, Pages 129 - 138, 2018. [Link]

[J11] P. Yu, W. B. Haskell, and H. Xu. Approximate value iteration for risk-aware Markov decision processes. IEEE Transactions on Automatic Control, 2017. [Link]

[J10] G. Yu, W. B. Haskell, and Y. Liu. Resilient facility location against the risk of disruptions. Transportation Research Part B, Vol. 104, Pages 82 - 105, 2017. [Link]

[J9] W. B. Haskell, J. G. Shanthikumar, and Z. Shen. Primal-dual algorithms for optimization with stochastic dominance. SIAM Journal on Optimization, Vol. 27, No. 1, Pages 34 - 66, 2017. [Link]

[J8] W. B. Haskell, J. G. Shanthikumar, and Z. Shen. Aspects of optimization with stochastic dominance. Annals of Operations Research, Vol. 253, No. 1, Pages 247 - 273, 2017. [Link]

[J7] J. Woodruff, W. B. Haskell, and A. Toriello. Optimized Financial Systems Helps Customers Meet their Personal Finance Goals with Optimization. Interfaces, Vol. 46, No. 4, Pages 345 - 359, 2016. [Link]

[J6] W. B. Haskell, L. Fu, and M. Dessouky. Ambiguity in risk preferences in robust stochastic optimization. European Journal of Operational Research, Vol. 254, No. 1, Pages 214 - 225, 2016. [Link]

[J5] W. B. Haskell, R. Jain, and D. Kalathil. Empirical Dynamic Programming. Mathematics of Operations Research, Vol. 41, No. 2, Pages 402 - 429, 2016. [Link]

[J4] W. B. Haskell and R. Jain. A convex analytic approach for risk-aware Markov decision processes. SIAM Journal on Control and Optimization, Vol. 53, No. 3, Pages 1569 - 1598, 2015. [Link]

[J3] A. Toriello, W. B. Haskell, and M. Poremba. A dynamic traveling salesman problem with stochastic arc costs. Operations Research, Vol. 62, No. 5, Pages 1107 - 1125, 2014. [Link]

[J2] W. B. Haskell and R. Jain. Stochastic dominance-constrained Markov decision processes. SIAM Journal on Control and Optimization, Vol. 51, No. 1, Pages 273 - 303, 2013. [Link]

[J1] W. B. Haskell, J. G. Shanthikumar, and Z. Shen. Optimization with a class of multivariate integral stochastic order constraints. Annals of Operations Research, Vol. 206, No. 1, Pages 147 - 162, 2013. [Link]

Working Papers

[W11] X. Zhang, W. B. Haskell, and Z. Ye. A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators. [arXiv]

[W10] W. B. Haskell, W. Huang, and H. Xu. Preference elicitation and robust optimization with multi-attribute quasi-concave choice functions. [arXiv]

[W9] S. Wang, S. Ng, and W. B. Haskell. A Multi-Level Simulation Optimization Approach for Quantile Functions. [arXiv]

[W8] H. Le, R. Zhao, and W. B. Haskell. Sequential smoothing framework for saddle-point problems with application to constrained optimization. [arXiv]

[W7] Z. Chen, P. Yu, and W. B. Haskell. Distributionally Robust Optimization for Sequential Decision Making. [arXiv]

[W6] S. Zhao, W. B. Haskell, and M. Cardin. An Approximate Dynamic Programming Algorithm for a Multi-stage Capacity Investment Problem. [arXiv]

[W5] B. Wei, W. B. Haskell, and S. Zhao. Corporative Stochastic Approximation with Random Constraint Sampling for Semi-Infinite Programming. [arXiv]

[W4] J. Isohatala and W. B. Haskell. Probabilistic risk-aware control of stochastic differential equations. [arXiv]

[W3] B. Wei, W. B. Haskell, and S. Zhao. Randomized Primal-Dual Algorithms for Semi-Infinite Programming. [arXiv]

[W2] W. Huang, H. Pham, and W. B. Haskell. Model and algorithm for time-consistent risk-aware Markov games. [arXiv]

[W1] S. Zhao, W. B. Haskell, and M. Cardin. A Flexible Multi-Facility Capacity Expansion Problem with Risk Aversion. [arXiv]

Conference Proceedings

[C20] R. Zhao, W. B. Haskell, and V. Tan. An Optimal Algorithm for Stochastic Three-Composite Optimization. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

[C19] S. Wang, S. Ng, and W. B. Haskell. Quantile Simulation Optimization with Stochastic Co-Kriging Model. Winter Simulation Conference, 2018.

[C18] S. Zhao, W. B. Haskell, and M. A. Cardin. An Approximate Dynamic Programming Approach for Multi-Facility Capacity Expansion Problem with Flexibility Design. IISE Annual Conference Proceedings, 2017. [Link]

[C17] H. Le and W. B. Haskell. Sequential smoothing framework for convex concave saddle point problems with application to large-scale constrained optimization. Allerton Conf. on Communication, Control, and Computing, 2017. [Link]

[C16] W. B. Haskell and R. Jain. Inexact iteration of averaged operators for non-strongly convex stochastic optimization. Allerton Conf. on Communication, Control, and Computing, 2017. [Link]

[C15] P. Yu, W. B. Haskell, and H. Xu. Dynamic Programming for Risk-aware Sequential Optimization. Proc. of the IEEE Control and Decision Conf. (CDC), 2017. [Link]

[C14] J. Isohatala and W. B. Haskell. Risk-aware semi-Markov decision processes. Proc. of the IEEE Control and Decision Conf. (CDC), 2017. [Link]

[C13] W. Huang and W. B. Haskell. Risk-aware Q-Learning for Markov Decision Processes. Proc. of the IEEE Control and Decision Conf. (CDC), 2017. [Link]

[C12] W. B. Haskell, P. Yu, R. Jain, and H. Sharma. Randomized function fitting for empirical dynamic programming. Proc. of the IEEE Control and Decision Conf. (CDC), 2017. [Link]

[C11] W. B. Haskell and R. Jain. A random monotone operator framework for strongly convex stochastic optimization. Proc. of the IEEE Control and Decision Conf. (CDC), 2017. [Link]

[C10] R. Zhao, W. B. Haskell, and V. Tan. Stochastic LBFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies. Conf. on Uncertainty in Artificial Intelligence (UAI), 2017. (Acceptance Rate: 30.9%) [Link]

[C9] W. B. Haskell, R. Jain, and H. Sharma. A Dynamical Systems Framework for Stochastic Iterative Optimization. Proc. of the IEEE Control and Decision Conf. (CDC), 2016. [Link]

[C8] Y. Quan, W. B. Haskell, and M. Tambe. Robust Strategy against Unknown Risk-averse Adversaries in Security Games. International Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2015. (Acceptance Rate: 27%) [Link]

[C7] M. Brown, W. B. Haskell, M. Tambe. Robust patrol generation for fishery protection. Conf. on Decision and Game Theory for Security (GameSec), 2014. [Link]

[C6] W. B. Haskell, D. Kar, F. Fang, M. Tambe, S. Cheung, and E. Denicola. Robust protection of fisheries with COmPASS. Innovative Applications of Artificial Intelligence (IAAI), 2014. [Link]

[C5] W. B. Haskell, R. Jain, and D. Kalathil. Empirical Value Iteration for Approximate Dynamic Programming. American Control Conf. (ACC), 2014. [Link]

[C4] Y. Quan, W. B. Haskell, A. Jiang, and M. Tambe. Online Planning for Optimal Protector Strategies in Resource Conservation Games. International Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2014. (Acceptance Rate: 12%) [Link]

[C3] J. Kwak, P. Varakantham, W. B. Haskell, D. Kar, and M. Tambe. Building THINC: User Incentivization and Meeting Rescheduling for Energy Savings. International Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2014. (Acceptance Rate: 12%) [Link]

[C2] W. B. Haskell and R. Jain. Dominance-constrained Markov decision processes. Proc. of the IEEE Control and Decision Conf. (CDC), 2012. [Link]

[C1] A. Sadovsky, H. Swenson, W. B. Haskell, and J. Rakas. Optimal time advance in terminal area arrivals: Throughput vs. fuel savings. Digital Avionics Systems Conference (DASC), 2011. [Link]

Book Chapters

[B1] W. B. Haskell and F. Ordonez. Mixed-integer optimization methods for solving Stackelberg security games. Improving Homeland Security Decisions. Cambridge University Press, 2016. [Link]