We address the relative paucity of empirical testing of learning algorithms (of any type) by introducing a new public-domain, Modular, Optimal Learning Testing Environment (MOLTE) for Bayesian ranking and selection problem, stochastic bandits or sequential experimental design problems. The Matlab-based simulator allows the comparison of a number of learning policies (represented as a series of .m modules) in the context of a wide range of problems (each represented in its own .m module) which makes it easy to add new algorithms and new test problems. State-of-the-art policies and various problem classes are provided in the package. The choice of problems and policies is guided through a spreadsheet-based interface. Different graphical metrics are included. MOLTE is designed to be compatible with parallel computing to scale up from local desktop to clusters and clouds. We offer MOLTE as an easy-to-use tool for the research community that will make it possible to perform much more comprehensive testing, spanning a broader selection of algorithms and test problems. We demonstrate the capabilities of MOLTE through a series of comparisons of policies on a starter library of test problems. We also address the problem of tuning and constructing priors that have been largely overlooked in optimal learning literature. We envision MOLTE as a modest spur to provide researchers an easy environment to study interesting questions involved in optimal learning.

**Click to Read Paper and Get Code*** in ISMB Bio-Ontologies, 2012

**Click to Read Paper and Get Code**

Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models

Nov 22, 2016

Xinyu He, Warren B. Powell

Nov 22, 2016

Xinyu He, Warren B. Powell

**Click to Read Paper and Get Code**

An optimal learning method for developing personalized treatment regimes

Jul 06, 2016

Yingfei Wang, Warren Powell

Jul 06, 2016

Yingfei Wang, Warren Powell

**Click to Read Paper and Get Code**

**Click to Read Paper and Get Code**

XNMR is a system designed to explore the results of combining the well-founded semantics system XSB with the stable-models evaluator SMODELS. Its main goal is to work as a tool for fast and interactive exploration of knowledge bases.

* 2 pages; no figures; NMR2000 Systems Description

* 2 pages; no figures; NMR2000 Systems Description

**Click to Read Paper and Get Code*** 8 pages. Submitted to 2019 AAAI Spring Symposium on Verification of Neural Networks

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ABox Abduction via Forgetting in ALC (Long Version)

Nov 13, 2018

Warren Del-Pinto, Renate A. Schmidt

Nov 13, 2018

Warren Del-Pinto, Renate A. Schmidt

* Long version of a paper accepted for publication in the proceedings of AAAI 2019

**Click to Read Paper and Get Code**

* Accepted to Asilomar 2018 - special session on "Machine Learning for Wireless Systems"

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Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks

Sep 13, 2017

Yingfei Wang, Chu Wang, Warren Powell

Sep 13, 2017

Yingfei Wang, Chu Wang, Warren Powell

* arXiv admin note: text overlap with arXiv:1510.02354

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* Published as a conference paper at the 2017 International Symposium on Information Theory (ISIT)

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* David S. Warren and Yanhong A. Liu (Editors). 33 pages. Including summaries by Christopher Kane and abstracts or position papers by M. Aref, J. Rosenwald, I. Cervesato, E.S.L. Lam, M. Balduccini, J. Lobo, A. Russo, E. Lupu, N. Leone, F. Ricca, G. Gupta, K. Marple, E. Salazar, Z. Chen, A. Sobhi, S. Srirangapalli, C.R. Ramakrishnan, N. Bj{\o}rner, N.P. Lopes, A. Rybalchenko, and P. Tarau

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Descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device

Mar 21, 2016

Warren Rieutort-Louis, Ognjen Arandjelovic

Mar 21, 2016

Warren Rieutort-Louis, Ognjen Arandjelovic

* 2016

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The Knowledge Gradient with Logistic Belief Models for Binary Classification

Oct 08, 2015

Yingfei Wang, Chu Wang, Warren Powell

Oct 08, 2015

Yingfei Wang, Chu Wang, Warren Powell

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Online Approximate Optimal Station Keeping of an Autonomous Underwater Vehicle

Apr 01, 2014

Patrick Walters, Warren E. Dixon

Online approximation of an optimal station keeping strategy for a fully actuated six degrees-of-freedom autonomous underwater vehicle is considered. The developed controller is an approximation of the solution to a two player zero-sum game where the controller is the minimizing player and an external disturbance is the maximizing player. The solution is approximated using a reinforcement learning-based actor-critic framework. The result guarantees uniformly ultimately bounded (UUB) convergence of the states and UUB convergence of the approximated policies to the optimal polices without the requirement of persistence of excitation.
Apr 01, 2014

Patrick Walters, Warren E. Dixon

* 6 pages

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Recursive Optimization of Convex Risk Measures: Mean-Semideviation Models

Oct 29, 2018

Dionysios S. Kalogerias, Warren B. Powell

Oct 29, 2018

Dionysios S. Kalogerias, Warren B. Powell

* 90 pages, 3 figures. Update: Substantial revision of the technical content, with an additional fully detailed analysis in regard to the rate of convergence of the MESSAGEp algorithm. NOTE: Please open in browser to see the math in the abstract!

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Consider a sample of $n$ points taken i.i.d from a submanifold $\Sigma$ of Euclidean space. We show that there is a way to estimate the Ricci curvature of $\Sigma$ with respect to the induced metric from the sample. Our method is grounded in the notions of Carr\'e du Champ for diffusion semi-groups, the theory of Empirical processes and local Principal Component Analysis.

* 47 pages

* 47 pages

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Clusters of Driving Behavior from Observational Smartphone Data

Jan 11, 2018

Josh Warren, Jeff Lipkowitz, Vadim Sokolov

Jan 11, 2018

Josh Warren, Jeff Lipkowitz, Vadim Sokolov

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Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures

May 09, 2017

Daniel R. Jiang, Warren B. Powell

May 09, 2017

Daniel R. Jiang, Warren B. Powell

* 39 pages, 7 figures

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Spherical Planetary Robot for Rugged Terrain Traversal

Feb 09, 2017

Laksh Raura, Andrew Warren, Jekan Thangavelautham

Feb 09, 2017

Laksh Raura, Andrew Warren, Jekan Thangavelautham

* 10 pages, 16 figures in Proceedings of the IEEE Aerospace Conference 2017

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