D. J. Lizotte and E. B. Laber, "Multi-Objective Markov Decision Processes for Data-Driven Decision Support," Submiss., 2015.

M. Cormier, D. J. Lizotte, and R. Mann, "Reconstruction of 3-D Density Functions from Few Projections: Structural Assumptions for Graceful Degradation," in Proceedings of the 12th Conference on Computer and Robot Vision, 2015.

R. Suderman, D. J. Lizotte, and N. M. Abukhdeir, "Theory and application of shapelets to the analysis of surface self-assembly imaging," Phys. Rev. E, vol. 91, no. 3, p. 33307, Mar. 2015.

E. B. Laber, D. J. Lizotte, and B. Ferguson, "Set-valued dynamic treatment regimes for competing outcomes," Biometrics, vol. 70, no. 1, pp. 53–61, Mar. 2014.

E. B. Laber, D. J. Lizotte, M. Qian, and S. A. Murphy, "Dynamic treatment regimes: technical challenges and applications," Electron. J. Stat., vol. 8, no. 0, pp. 1225–1272, 2014.

G. Zhu, D. J. Lizotte, and J. Hoey, "Scalable Approximate Policies for Markov Decision Process Models of Hospital Elective Admissions," Artif. Intell. Med., vol. 61, no. 1, pp. 21–34, May 2014.

Tameem Adel, Ruth Urner, Benn Smith, Daniel Stashuk, and Daniel J. Lizotte. Generative multiple-instance learning models for quantitative electromyography. In Ann Nicholson and Padhraic Smyth, editors, Proceedings of the 29th conference on Uncertainty in Artificial Intelligence (UAI), pages 2–11, Corvallis, Oregon, 2013. AUAI Press. Selected for oral oresentation.

Luiza Antonie, Kris Inwood, Daniel J. Lizotte, and J. Andrew Ross. Tracking people over time in 19th century Canada for longitudinal analysis. Machine Learning, November 2013. Online first.

Adedamola Adepetu, Elnaz Rezaei, Daniel Lizotte, and Srinivasan Keshav. Critiquing time-of-use pricing in Ontario. In IEEE SmartGridComm Symposium. IEEE Press, 2013.

Daniel J. Lizotte, Michael Bowling, Susan Murphy. Linear Fitted-Q Iteration with Multiple Reward Functions. Journal of Machine Learning Research 13 (Nov):3253-3295, 2012.

Daniel Almirall, Daniel J. Lizotte, and Susan A. Murphy. Comment on “Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer” by L. Wang, A. Rotnitzky, X. Lin, R. E. Millikan and P. F. Thall. Journal of the American Statistical Association, 107(498):509–512, 2012.

Rayman Preet Singh, Peter Xiang Gao and Daniel J. Lizotte. On Hourly Home Peak Load Prediction. IEEE SmartGridComm 2012. Best Paper Award.

John A. Doucette, Atif Khan, Robin Cohen, Dan Lizotte and Hooman Mohajeri Moghaddam. A Framework for AI-Based Clinical Decision Support that is Patient-Centric and Evidence-Based. NETMED: 1st International Workshop on Artificial Intelligence and NetMedicine, 2012.

A. Khan, J.A. Doucette, R. Cohen, and D.J. Lizotte. Integrating machine learning into a medical decision support system to address the problem of missing patient data. In 11th International Conference on Machine Learning and Applications (ICMLA), volume 1, pages 454–457, Dec. 2012.

Daniel J. Lizotte, Russell Greiner, and Dale Schuurmans. An experimental methodology for response surface optimization methods. The Journal of Global Optimization, Volume 53, Number 4 (2012), 699-736, DOI: 10.1007/s10898-011-9732-z

Daniel J. Lizotte. Convergent Fitted Value Iteration with Linear Function Approximation. Neural Information Processing Systems 24, 2011.

Susan M. Shortreed, Eric Laber, Daniel J. Lizotte, Scott Stroup, Joelle Pineau, and Susan A. Murphy. Informing sequential clinical decision-making through reinforcement learning: an empirical study. Machine Learning Journal, 2010.

Daniel J. Lizotte, Michael Bowling, and Susan A. Murphy. Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Clinical Trial Analysis. Proceedings of the Twenty-Seventh International Conference on Machine Learning (ICML), 2010. See also: Talk, Poster.

Daniel J. Lizotte, Eric Laber, A. John Rush and Susan A. Murphy. Exploratory Analysis of Practical Trial Data for Informing the Individualization Of Treatment. Penn. State Methodology Center Technical Report 09-96.

Daniel J. Lizotte, Eric Laber and Susan A. Murphy. Assessing Confidence in Policies Learned from Sequential Randomized Trials. Technical report 481, Department of Statistics, University of Michigan.

D. Lizotte, L. Gunter, E. Laber and S.A. Murphy. Missing Data and Uncertainty in Batch Reinforcement Learning. Selected for a poster presentation at the NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning.

Daniel Lizotte. Practical Bayesian optimization. PhD thesis, University of Alberta, 2008.

Daniel Lizotte, Tao Wang, Michael Bowling, and Dale Schuurmans. Automatic gait optimization with Gaussian process regression. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), 2007.

Tao Wang, Daniel Lizotte, Michael Bowling, and Dale Schuurmans. Stable dual dynamic programming. In Advances in Neural Information Processing Systems (NIPS*2007), 2007.

Qin Wang, Colin Cherry, Daniel Lizotte, and Dale Schuurmans. Improved large margin dependency parsing via local constraints and Laplacian regularization. In Proceedings of the Tenth Conference on Computational Natural Language Learning (CONLL-06), pages 21-28, 2006.

Tao Wang, Daniel Lizotte, Michael Bowling, and Dale Schuurmans. Bayesian sparse sampling for on-line reward optimization. In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML), pages 961-968, 2005.

Omid Madani, Daniel Lizotte, and Russell Greiner. Active model selection. In Proceedings of the 20th conference on Uncertainty in Artificial Intelligence (UAI), 2004.

Daniel Lizotte. Budgeted learning of naïve Bayes classifiers. Master's thesis, University of Alberta, 2003.

Daniel Lizotte, Omid Madani, and Russell Greiner. Budgeted learning of naïve-Bayes classifiers. In 19th Conference on Uncertainty in Artificial Intelligence (UAI), 2003.

Daniel Lizotte, Eric Aubanel, and Virendra Bhavsar. High Performance Computing Systems and Applications, chapter 12: Nonuniform DFT Applications in MRI: Parallel Algorithms and Implementations on the IBM SP. Robert D. Kent and Todd W. Sands, eds. Pp. 41-54. Kluwer Academic Publishers, Norwell, MA, 2003.

Daniel Lizotte and Hong Zhang. Trading confidence for communications. In IEEE International Conference on Systems, Man and Cybernetics (SMC), volume 1, pages 935Publishers, Norwell, MA, 2003.-940, 2003.

Daniel Lizotte, Lawrence Garey, and Ruth Shaw. A parallel numerical algorithm for boundary-value FIDEs on a PC cluster. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS), pages 397-402, 2002.

Ruth E. Shaw, Lawrence E. Garey, and Daniel J. Lizotte. A parallel numerical algorithm for Fredholm integro-differential two-point boundary value problems. The International Journal of Computer Mathematics, 77:305-318, 2001.