Publications

Below is a collection of my publications as well as any preprints or technical reports.

Conference and workshop proceedings

  1. Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., & de Freitas, N. (2016). Learning to learn by gradient descent by gradient descent. In Neural Information Processing Systems. [pdf] [bibtex]

  2. Hoffman, M. W., & Ghahramani, Z. (2015). Output-Space Predictive Entropy Search for Flexible Global Optimization. In the NIPS workshop on Bayesian optimization. [pdf] [bibtex]

  3. Hernández-Lobato, J. M., Gelbart, M. A., Hoffman, M. W., Adams, R. P., & Ghahramani, Z. (2015). Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. In the International Conference on Machine Learning. [pdf] [bibtex]

  4. Hoffman, M. W., & Shahriari, B. (2014). Modular mechanisms for Bayesian optimization. In the NIPS workshop on Bayesian optimization. [pdf] [bibtex]

  5. Hernández-Lobato, J. M., Hoffman, M. W., & Ghahramani, Z. (2014). Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. In Neural Information Processing Systems. [pdf] [bibtex]

  6. Hoffman, M. W., Shahriari, B., & de Freitas, N. (2014). On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In the International Conference on Artificial Intelligence and Statistics (pp. 365–374). [pdf] [bibtex]

  7. Hoffman, M. W., Lazaric, A., Ghavamzadeh, M., & Munos, R. (2012). Regularized Least Squares Temporal Difference Learning with Nested ell_2 and ell_1 Penalization. In the European Workshop on Reinforcement Learning (pp. 102–114). [pdf] [bibtex]

  8. Ghavamzadeh, M., Lazaric, A., Hoffman, M. W., & Munos, R. (2011). Finite-Sample Analysis of Lasso-TD. In the International Conference on Machine Learning (pp. 1177–1184). [pdf] [bibtex]

  9. Hoffman, M. W., Brochu, E., & de Freitas, N. (2011). Portfolio Allocation for Bayesian Optimization. In Uncertainty in Artificial Intelligence (pp. 327–336). [pdf] [bibtex]

  10. Hoffman, M. W., Kueck, H., de Freitas, N., & Doucet, A. (2009). New inference strategies for solving Markov decision processes using reversible jump MCMC. In Uncertainty in Artificial Intelligence (pp. 223–231). [pdf] [bibtex]

  11. Hoffman, M. W., de Freitas, N., Doucet, A., & Peters, J. (2009). An Expectation Maximization algorithm for continuous Markov Decision Processes with arbitrary reward. In the International Conference on Artificial Intelligence and Statistics (pp. 232–239). [pdf] [code] [bibtex]

  12. Hoffman, M. W., Doucet, A., de Freitas, N., & Jasra, A. (2007). Bayesian policy learning with trans-dimensional MCMC. In Neural Information Processing Systems (pp. 665–672). [pdf] [bibtex]

  13. Shon, A. P., Grimes, D. B., Baker, C. L., Hoffman, M. W., Zhou, S., & Rao, R. P. N. (2005). Probabilistic gaze imitation and saliency learning in a robotic head. In International Conference on Robotics and Automation (pp. 2865–2870). [pdf] [bibtex]

Journal articles

  1. Hoffman, M. W., Grimes, D. B., Shon, A. P., & Rao, R. P. N. (2006). A probabilistic model of gaze imitation and shared attention. Neural Networks, 19, 299–310. [pdf] [bibtex]

Book chapters

  1. Hoffman, M. W., & de Freitas, N. (2012). Inference strategies for solving semi-Markov decision processes. In L. E. Sucar, E. F. Morales, & J. Hoey (Eds.), Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions. IGI Global. [pdf] [bibtex]

  2. Kueck, H., Hoffman, M. W., Doucet, A., & de Freitas, N. (2009). Inference and Learning for Active Sensing, Experimental Design and Control. In Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis (pp. 1–10). [pdf] [bibtex]

Technical reports

  1. Shahriari, B., Wang, Z., Hoffman, M. W., Bouchard-Côté, A., & de Freitas, N. (2015). An Entropy Search Portfolio for Bayesian Optimization. arXiv:1406.4625. [pdf] [bibtex]

  2. Hoffman, M. W., Doucet, A., de Freitas, N., & Jasra, A. (2007). On solving general state-space sequential decision problems using inference algorithms (No. TR-2007-04). University of British Columbia, Computer Science. [pdf] [bibtex]

Thesis

  1. Hoffman, M. W. (2013). Decision making with inference and learning methods (PhD thesis). University of British Columbia. [pdf] [bibtex]