#### Filter Results:

- Full text PDF available (33)

#### Publication Year

2006

2017

- This year (2)
- Last 5 years (20)
- Last 10 years (38)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

- Ilya O. Ryzhov, Warren B. Powell, Peter I. Frazier
- Operations Research
- 2012

We derive a one-period look-ahead policy for finiteand infinite-horizon online optimal learning problems with Gaussian rewards. Our approach is able to handle the case where our prior beliefs about the rewards are correlated, which is not handled by traditional multi-armed bandit methods. Experiments show that our KG policy performs competitively againstâ€¦ (More)

- Ilya O. Ryzhov, Warren B. Powell
- 2009 IEEE Symposium on Adaptive Dynamicâ€¦
- 2009

We derive a one-period look-ahead policy for online subset selection problems, where learning about one subset also gives us information about other subsets. The subset selection problem is treated as a multi-armed bandit problem with correlated prior beliefs. We show that our decision rule is easily computable, and present experimental evidence that theâ€¦ (More)

- Huashuai Qu, Ilya O. Ryzhov, Michael C. Fu, Zi Ding
- Operations Research
- 2015

- Ilya O. Ryzhov
- Operations Research
- 2016

We consider a ranking and selection problem with independent normal observations, and analyze the asymptotic sampling rates of expected improvement (EI) methods in this setting. Such methods often perform well in practice, but a tractable analysis of their convergence rates is difficult due to the nonlinearity and nonconvexity of the functions used in theâ€¦ (More)

- Ilya O. Ryzhov, Warren B. Powell
- Operations Research
- 2011

We derive a knowledge gradient policy for an optimal learning problem on a graph, in which we use sequential measurements to refine Bayesian estimates of individual edge values in order to learn about the best path. This problem differs from traditional ranking and selection, in that the implementation decision (the path we choose) is distinct from theâ€¦ (More)

- Marie Chau, Michael C. Fu, Huashuai Qu, Ilya O. Ryzhov
- Proceedings of the Winter Simulation Conferenceâ€¦
- 2014

We provide a tutorial overview of simulation optimization methods, including statistical ranking & selection (R&S) methods such as indifference-zone procedures, optimal computing budget allocation (OCBA), and Bayesian value of information (VIP) approaches; random search methods; sample average approximation (SAA); response surface methodology (RSM);â€¦ (More)

- Ilya O. Ryzhov, Peter I. Frazier, Warren B. Powell
- ICCS
- 2010

- Ilya O. Ryzhov, Warren B. Powell
- 2010 48th Annual Allerton Conference onâ€¦
- 2010

In approximate dynamic programming, we can represent our uncertainty about the value function using a Bayesian model with correlated beliefs. Thus, a decision made at a single state can provide us with information about many states, making each individual observation much more powerful. We propose a new exploration strategy based on the knowledge gradientâ€¦ (More)

- Michael C. Fu, GÃ¼zin Bayraksan, +4 authors Benjamin G. Thengvall
- Proceedings of the Winter Simulation Conferenceâ€¦
- 2014

The goal of this panel was to discuss the state of the art in simulation optimization research and practice. The participants included representation from both academia and industry, where the latter was represented by participation from a leading software provider of optimization tools for simulation. This paper begins with a short introduction toâ€¦ (More)

- Ilya O. Ryzhov, Martin R. Valdez-Vivas, Warren B. Powell
- Proceedings of the 2010 Winter Simulationâ€¦
- 2010

We examine a newsvendor problem with two agents: a requesting agent that observes private demand information, and an oversight agent that must determine how to allocate resources upon receiving a bid from the requesting agent. Because the two agents have different cost structures, the requesting agent tends to bid higher than the amount that is actuallyâ€¦ (More)