Conferences

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]

Conference Presentations

[P2] H. Le, R. Zhao, and W. B. Haskell. An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems. International Conference on Continuous Optimization (ICCOPT), August 7, 2019. [Slides]

[P1] J. Isohatala and W. B. Haskell. Risk aware minimum principle for optimal control of stochastic differential equations. International Conference on Stochastic Programming (ICSP), July 30, 2019. [Slides]