Monte Carlo: Publications

Book chapters

• [1]

  Inference and Learning for Active Sensing‚ Experimental Design and Control

  Hendrik Kueck‚ Matt Hoffman‚ Arnaud Doucet and Nando Freitas

  In Helder Araujo‚ Ana Maria Mendonca‚ Armando J. Pinho and Maria Ines Torres, editors, Pattern Recognition and Image Analysis. Vol. 5524 of Lecture Notes in Computer Science. Pages 1–10. Springer Berlin Heidelberg. 2009.

  Details about Inference and Learning for Active Sensing‚ Experimental Design and Control | BibTeX data for Inference and Learning for Active Sensing‚ Experimental Design and Control | DOI (10.1007/978-3-642-02172-5_1) | Link to Inference and Learning for Active Sensing‚ Experimental Design and Control

Journal papers

• [1]

  Self−Avoiding Random Dynamics on Integer Complex Systems

  Firas Hamze‚ Ziyu Wang and Nando de Freitas

  In ACM Transactions on Modelling and Computer Simulation. Vol. 23. No. 1. Pages 9:1–9:25. 2013.

  Details about Self−Avoiding Random Dynamics on Integer Complex Systems | BibTeX data for Self−Avoiding Random Dynamics on Integer Complex Systems | DOI (10.1145/2414416.2414790) | Link to Self−Avoiding Random Dynamics on Integer Complex Systems

• [2]

  Adaptive MCMC with Bayesian Optimization

  Nimalan Mahendran‚ Ziyu Wang‚ Firas Hamze and Nando de Freitas

  In Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS). Vol. 22. Pages 751–760. 2012.

  Details about Adaptive MCMC with Bayesian Optimization | BibTeX data for Adaptive MCMC with Bayesian Optimization | Link to Adaptive MCMC with Bayesian Optimization

Conference papers

• [1]

  Herded Gibbs Sampling

  Luke Bornn‚ Yutian Chen‚ Nando de Freitas‚ Mareija Eskelin‚ Jing Fang and Max Welling

  In International Conference on Learning Representations (ICLR). 2013.

  Details about Herded Gibbs Sampling | BibTeX data for Herded Gibbs Sampling | Link to Herded Gibbs Sampling

• [2]

  Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers

  Ziyu Wang‚ Shakir Mohamed and Nando de Freitas

  In International Conference on Machine Learning (ICML). Pages 1462–1470. 2013.

  JMLR &CPW 28 (3): 1462–1470‚ 2013

  Details about Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers | BibTeX data for Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers | Download (pdf) of Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers

• [3]

  New inference strategies for solving Markov Decision Processes using reversible jump MCMC

  Matthias Hoffman‚ Hendrik Kueck‚ Nando de Freitas and Arnaud Doucet

  In Uncertainty in Artificial Intelligence (UAI). Pages 223–231. Corvallis‚ Oregon. 2009.

  Details about New inference strategies for solving Markov Decision Processes using reversible jump MCMC | BibTeX data for New inference strategies for solving Markov Decision Processes using reversible jump MCMC | Link to New inference strategies for solving Markov Decision Processes using reversible jump MCMC

• [4]

  Conditional mean field

  Peter Carbonetto and Nando De Freitas

  In B. Schölkopf‚ J. Platt and T. Hoffman, editors, Advances in Neural Information Processing Systems (NIPS). Pages 201–208. Cambridge‚ MA. 2006. MIT Press.

  Details about Conditional mean field | BibTeX data for Conditional mean field | Download (pdf) of Conditional mean field

• [5]

  Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs

  Firas Hamze and Nando De Freitas

  In Y. Weiss‚ B. Schölkopf and J. Platt, editors, Advances in Neural Information Processing Systems (NIPS). Pages 491–498. Cambridge‚ MA. 2005. MIT Press.

  Details about Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs | BibTeX data for Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs | Download (pdf) of Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs

• [6]

  A Boosted Particle Filter: Multitarget Detection and Tracking

  Kenji Okuma‚ Ali Taleghani‚ Nando Freitas‚ James J. Little and David G. Lowe

  In Tomas Pajdla and Jiri Matas, editors, Computer Vision − ECCV 2004. Vol. 3021 of Lecture Notes in Computer Science. Pages 28–39. Springer Berlin Heidelberg. 2004.

  Best Paper prize in Cognitive Vision

  Details about A Boosted Particle Filter: Multitarget Detection and Tracking | BibTeX data for A Boosted Particle Filter: Multitarget Detection and Tracking | DOI (10.1007/978-3-540-24670-1_3) | Link to A Boosted Particle Filter: Multitarget Detection and Tracking

• [7]

  From Fields to Trees

  Firas Hamze and Nando de Freitas

  In Uncertainty in Artificial Intelligence (UAI). Pages 243–250. Arlington‚ Virginia. 2004. AUAI Press.

  Details about From Fields to Trees | BibTeX data for From Fields to Trees | Download (pdf) of From Fields to Trees

• [8]

  Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks

  Arnaud Doucet‚ Nando de Freitas‚ Kevin Murphy and Stuart Russell

  In Uncertainty in Artificial Intelligence (UAI). Pages 176–183. San Francisco‚ CA. 2000. Morgan Kaufmann.

  Details about Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks | BibTeX data for Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks | Download (pdf) of Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks

• [9]

  The Unscented Particle Filter

  Rudolph van der Merwe‚ Arnaud Doucet‚ Nando de Freitas and Eric A. Wan

  In Advances in Neural Information Processing Systems (NIPS). Pages 584–590. 2000.

  Details about The Unscented Particle Filter | BibTeX data for The Unscented Particle Filter | Download (pdf) of The Unscented Particle Filter