2012

• Firas Hamze, Ziyu Wang and Nando de Freitas. Self-Avoiding Random Dynamics on Integer Complex Systems. ACM Transactions on Modeling and Computer Simulation. An older version appeared as an arxiv technical report.

• Nando de Freitas, Alex Smola and Masrour Zoghi. Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. ICML. An older version appeared as Technical Report arXiv:1203.2177v1. [Feedback Page]

• Misha Denil, Loris Bazzani, Hugo Larochelle and Nando de Freitas. Learning Where to Attend with Deep Architectures for Image Tracking. Neural Computation, Vol. 24, No. 8 , Pages 2151-2184. An older version appeared as an arxiv technical report. [BibTex]

• Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence
  Nando de Freitas and Kevin Murphy (eds)
  

• Michael A. Osborne, Roman Garnett, Kevin Swersky and Nando de Freitas. Prediction and fault detection of environmental signals with uncharacterised faults. AAAI Conference on Artificial Intelligence. An appendix to this paper is also available [BibTex]

• Mohamed Ahmed, Pouyan Bibalan, Nando de Freitas and Simon Fauvel. Decentralized, Adaptive, Look-Ahead Particle Filtering. Technical Report arXiv:1203.2394v1 [BibTex]

• Byron Knoll and Nando de Freitas. A Machine Learning Perspective on Predictive Coding with PAQ. Data Compression Conference (DCC). Older version appeared as Technical Report arXiv:1108.3298v1. [BibTex]

• Nimalan Mahendran, Ziyu Wang, Firas Hamze and Nando de Freitas Bayesian Optimization for Adaptive MCMC. AI and Statistics. Older version appeared as Technical Report arXiv:1110.6497v1 [BibTex]

• David Buchman, Mark Schmid, Shakir Mohamed, David Poole and Nando de Freitas On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. AI and Statistics. [BibTex]

 

 

2011

• Misha Denil and Nando de Freitas. Toward the Implementation of a Quantum RBM. NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop.

• Ziyu Wang and Nando de Freitas. Predictive Adaptation of Hybrid Monte Carlo with Bayesian Parametric Bandits. NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop.

• Ben Marlin and Nando de Freitas. Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models. UAI. [BibTex]

• Eric Brochu, Matt Hoffman and Nando de Freitas. Portfolio Allocation for Bayesian Optimization. UAI. [BibTex]

• Michael Osborne, Roman Garnett, Kevin Swersky and Nando de Freitas. A Machine Learning Approach to Pattern Detection and Prediction for Environmental Monitoring and Water Sustainability. ICML Workshop on Machine Learning for Global Challenges.

• Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin Marlin, and Nando de Freitas. On Autoencoders and Score Matching for Energy Based Models. ICML. [Appendix] [BibTex]

• Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino and Jo-Anne Ting. Learning attentional policies for tracking and recognition in video with deep networks. ICML. [videos] [BibTex]

 

 

2010

• Benjamin Marlin, Kevin Swersky, Bo Chen and Nando de Freitas. Inductive Principles for Restricted Boltzmann Machine Learning. AISTATS.

• Firas Hamze and Nando de Freitas. Intracluster Moves for Constrained Discrete-Space MCMC. Uncertainty in Artificial Intelligence (UAI). [BibTex]

• Eric Brochu, Tyson Brochu and Nando de Freitas. A Bayesian Interactive Optimization Approach to Procedural Animation Design. ACM SIGGRAPH/Eurographics Symposium on Computer Animation. [BibTex] [video]

• Bo Chen, Jo-Anne Ting, Ben Marlin and Nando de Freitas Deep Learning of Invariant Spatio-Temporal Features from Video. NIPS 2010 Deep Learning and Unsupervised Feature Learning Workshop, organized by Honglak Lee, Marc'Aurelio Ranzato, Yoshua Bengio, Geoff Hinton, Yan LeCun and Andrew Y. Ng. [denoising video] [spatio-temporal filters]

• Matt Hoffman and Nando de Freitas. Inference strategies for solving semi-Markov decision processes. To appear in Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, L.E. Sucar, E. Morales, H. Hoey (Eds.)

• Hendrik Kueck and Nando de Freitas. Where do priors and causal models come from? An experimental design perspective. Technical Report TR-2010-06. University of British Columbia, Department of Computer Science.

• Bo Chen, Kevin Swersky, Benjamin Marlin and Nando de Freitas. Sparsity priors and boosting for learning localized distributed feature representations. Technical Report TR-2010-04. University of British Columbia, Department of Computer Science.

• Kevin Swersky, Bo Chen, Benjamin Marlin, and Nando de Freitas. A Tutorial on Stochastic Approximation Algorithms for Training Restricted Boltzmann Machines and Deep Belief Nets. Information Theory and Applications (ITA) Workshop. [BibTex]

 

 

2009

• Eric Brochu, Vlad Cora and Nando de Freitas. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. Technical Report TR-2009-023. University of British Columbia, Department of Computer Science. [BibTex]

• Ruben Martinez-Cantin, Nando de Freitas, Eric Brochu, Jose Castellanos and Arnaud Doucet. A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Autonomous Robots. [BibTex]

• Matt Hoffman, Hendrik Kueck, Arnaud Doucet and Nando de Freitas. New inference strategies for solving Markov decision processes using reversible jump MCMC. UAI 2009. [BibTex]

• Hendrik Kueck, Matt Hoffman, Arnaud Doucet and Nando de Freitas. Inference and Learning for Active Sensing, Experimental Design and Control. Invited paper, IBPRIA 2009. [BibTex]

• Matt Hoffman, Nando de Freitas, Arnaud Doucet and Jan Peters. An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Rewards. AI-STATS 2009. [BibTex]

 

 

2008

• Peter Carbonetto, Mark Schmidt and Nando de Freitas. An interior-point stochastic approximation method and an L1-regularized delta rule. Neural Information Processing Systems (NIPS), 2008. [BibTex]

• Julia Vogel and Nando de Freitas. Target-directed attention: sequential decision-making for gaze planning. International Conference on Robotics and Automation (ICRA), 2007. [BibTex]

 

 

2007

• Matthew Hoffman, Arnaud Doucet, Nando de Freitas and Ajay Jasra. Bayesian Policy Learning with Trans-Dimensional MCMC. Advances in Neural Information Processing Systems (NIPS), 2007. [BibTex]

• Matthew Hoffman, Arnaud Doucet, Nando de Freitas and Ajay Jasra. On Solving General State-Space Sequential Decision Problems using Inference Algorithms. Technical Report UBC CS TR-2007-04. March 08, 2007.

• Eric Brochu, Nando de Freitas and Abhijeet Ghosh. Active Preference Learning with Discrete Choice Data. Advances in Neural Information Processing Systems (NIPS), 2007. [BibTex]

• Eric Brochu, Abhijeet Ghosh and Nando de Freitas. Preference Galleries for Material Design. ACM SIGGRAPH Sketch. [Poster] [BibTex] Winner of the SRC competition at SIGGRAPH.
• Firas Hamze and Nando de Freitas. Large-Flip Sampling. Uncertainty in Artificial Intelligence (UAI). [BibTex]

• Peter Carbonetto, Gyuri Dork, Cordelia Schmid, Hendrik Kck and Nando de Freitas. Learning to recognize objects with little supervision. International Journal of Computer Vision. [BibTex] [Sofware]

• Ruben Martinez-Cantin, Nando de Freitas, Arnaud Doucet and Jose Castellanos. Active Policy Learning for Robot Planning and Exploration under Uncertainty. Robotics: Science and Systems (RSS). [BibTex]

• Ruben Martinez-Cantin, Jose Castellanos and Nando de Freitas. Multi-Robot Marginal-SLAM. IJCAI Workshop on Multi-Robotic Systems for Societal Applications.

• Ruben Martinez-Cantin, Jose Castellanos and Nando de Freitas. Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM. International Conference on Robotics and Automation (ICRA), 2007. [BibTex]

 

2006

• Peter Carbonetto and Nando de Freitas. Conditional Mean Field. Advances in Neural Information Processing Systems (NIPS), 2006. [BibTex]

• Hendrik Kueck, Nando de Freitas and Arnaud Doucet SMC Samplers for Bayesian Optimal Nonlinear Design. Nonlinear Statistical Signal Processing Workshop (NSSPW), 2006. . [Software]. [BibTex]

• Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell and Dustin Lang. Fast Particle Smoothing: If I Had a Million Particles. ICML 2006. . [Sofware] [BibTex]

• Peter Carbonetto, Gyuri Dorko, Cordelia Schmid, Hendrik Kueck and Nando de Freitas. A Semi-Supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues. In Toward Category-Level Object Recognition, pages 277-300. 2006. [Software for Semi-supervised classification using a Bayesian kernel machine and data association constraints] [BibTex]

• Yizheng Cai, Nando de Freitas and Jim Little. Robust Visual Tracking for Multiple Targets. ECCV 2006. [Software, data and videos for the boosted particle filter] [BibTex]

• Firas Hamze, Jean-Noel Rivasseau and Nando de Freitas. Information Theory Tools to Rank MCMC Algorithms on Probabilistic Graphical Models . UCSD Information Theory Workshop, 2006. [Software for undirected probabilistic graphical models: Loopy, Gibbs and tree sampling]

 

 

2005

• Albert Jiang, Kevin Leyton-Brown and Nando de Freitas. N-Body Games. Published at the NIPS workshop on Game Theory, Machine Learning and Reasoning under Uncertainty.
• Firas Hamze and Nando de Freitas. Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs. NIPS 2005. [BibTex]
• Nando de Freitas, Yang Wang, Maryam Mahdaviani and Dustin Lang. Fast Krylov Methods for N-Body Learning . NIPS 2005. [KD-trees and fast multipole software]
[BibTex]
• Peter Carbonetto, Jacek Kisynski, Nando de Freitas and David Poole. Nonparametric Bayesian Logic . UAI 2005. [BibTex]

• Hendrik Kueck and Nando de Freitas. Learning to Classify Individuals Based on Group Statistics . UAI 2005. [BibTex]

• Mike Klaas, Nando de Freitas and Arnaud Doucet. Toward Practical N^2 Monte Carlo: The Marginal Particle Filter . UAI 2005. [Software] [BibTex]

• Dustin Lang, Mike Klaas and Nando de Freitas. Empirical Testing of Fast Kernel Density Estimation Algorithms. . UBC TR-2005-03. [Software]

• Mike Klaas, Dustin Lang and Nando de Freitas. Fast Maximum a Posteriori Inference in Monte Carlo State Spaces . AISTATS 2005. [Software]

• Maryam Mahdaviani, Nando de Freitas, Bob Fraser and Firas Hamze. Fast Computational Methods for Visually Guided Robots. ICRA 2005. [N-body software]

[BibTex]

 

2004

• Dustin Lang and Nando de Freitas. Beat Tracking the Graphical Model Way. NIPS 2004. [BibTex]

• Firas Hamze and Nando de Freitas. From Fields to Trees: On blocked and collapsed MCMC algorithms for undirected probabilistic graphical models. UAI 2004. [Tree sampling software] [BibTex]

• Kenji Okuma, Ali Taleghani, Nando de Freitas, Jim Little and David Lowe. A Boosted Particle Filter: Multitarget Detection and Tracking. ECCV 2004. mpg video 1 mpg video 2 Best Paper prize in Cognitive Vision. [Software, data and videos for the boosted particle filter] [BibTex]

• Peter Carbonetto, Nando de Freitas and Kobus Barnard. A Statistical Model for General Contextual Object Recognition. ECCV 2004. [software for image translation] [BibTex]

• Hendrik Kueck, Peter Carbonetto and Nando de Freitas. A Constrained Semi-Supervised Learning Approach to Data Association. ECCV 2004. [BibTex]

• Nando de Freitas, Richard Dearden, Frank Hutter, Ruben Morales-Menendez, Jim Mutch and David Poole. Diagnosis by a waiter and a Mars explorer. Invited paper for Proceedings of the IEEE, special issue on sequential state estimation. Vol 92 No 3, 2004. [Software for dynamic mixtures of Gaussians]

[BibTex]

 

2003

• Peter Carbonetto and Nando de Freitas. Why can't José read? The problem of learning semantic associations in a robot environment. Human Language Technology Conference Workshop on Learning Word Meaning from Non-Linguistic Data, 2003. [Software for image translation] [BibTex]

• Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei and Michael I. Jordan. Matching Words and Pictures. html Journal of Machine Learning Research (JMLR). [BibTex]

• Ruben Morales-Menendez, Nando de Freitas and David Poole. Estimation and Control of Industrial Processes with Particle Filters. American Control Conference, 2003. [Software for dynamic mixtures of Gaussians] [BibTex]

• Eric Brochu, Nando de Freitas and Kejie Bao. The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval. AI-STATS. PS

• Peter Carbonetto, Nando de Freitas, Paul Gustafson and Natalie Thompson. Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition. AI-STATS. [software for simultaneous feature weighting and clustering]

• Pinar Muyan and Nando de Freitas. A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference. AI-STATS. PS

 

 

2002

• Pinary Duygulu, Kobus Barnard, Nando de Freitas and David Forsyth. Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. ECCV 2002. [BibTex] Best Paper prize in Cognitive Vision.

• Christophe Andrieu, Nando de Freitas, Arnaud Doucet and Michael I. Jordan. An Introduction to MCMC for Machine Learning . Machine Learning, 2002. PS [BibTex]

• Ruben Morales-Menendez, Nando de Freitas and David Poole. Real-Time Monitoring of Complex Industrial Processes with Particle Filters. NIPS 2002. [BibTex] Mencion Especial - Romulo Garza Award

• Nando de Freitas. Rao-Blackwellised particle filtering for fault diagnosis. IEEE Aerospace Conference, 2002. [BibTex]

 

 

2001

• Arnaud Doucet, Nando de Freitas & Neil Gordon (eds). Sequential Monte Carlo Methods in Practice. Springer-Verlag. 2001. ISBN 0-387-95146-6.
  
  Sequential Monte Carlo methods, also known as bootstrap filters, condensation, particle filters and survival of the fittest, made it possible to numerically solve many complex, non-standard problems that were previously intractable. This book presented the first comprehensive and coherent treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks and time series analysis.
  
  When I began my work in this field in the late 90's, I realized that there were incomplete bits of the methodology in many fields. These fields weren't talking to each other. To make progress in all those fields, it was important to bring people together. So instead of writing the book, I chose to only write a few chapters and invite people in many other areas to contribute a chapter. It worked! The rest is history. Arnaud, Neil and I are pretty pleased with the results. The methodology is now more developed and coherent accross many disciplines.
  
  Citeseer lists this book as the 5th most cited article in computer science in 2000.

• Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Radial Basis Networks. Neural Computation. pages 2359-2407, 13(10). [BibTex]

• Christophe Andrieu, Nando de Freitas, Arnaud Doucet. Rao-Blackwellised Particle Filtering via Data Augmentation. Advances in Neural Information Processing Systems (NIPS13), 2001. [Longer report] [BibTex]

• Nando de Freitas,Pedro Højen-Sørensen, Michael Jordan and Stuart Russell. Variational MCMC. Uncertainty in Artificial Intelligence, 2001. . Longer version [BibTex]

 

 

2000

• R van der Merwe, A Doucet, Nando de Freitas and E Wan. The Unscented Particle Filter. Advances in Neural Information Processing Systems (NIPS13). T.K. Leen, T.G. Dietterich and V. Tresp editors. December, 2000. [BibTex]. Longer report [Software]

• Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Reversible Jump MCMC Simulated Annealing for Neural Networks. Uncertainty in Artificial Intelligence (UAI2000). [BibTex]

• Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. Uncertainty in Artificial Intelligence (UAI2000). [BibTex]. Also: A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. . This detailed discussion of the ABC network should complement the UAI2000 paper. [Slides] [Software].

• Nando de Freitas and Christophe Andrieu. Sequential Monte Carlo for Model Selection and Estimation of Neural Networks. ICASSP2000. [BibTex]

• Nando de Freitas, Mahesan Niranjan and Andrew Gee. Dynamic Learning With the EM Algorithm for Neural Networks. VLSI Signal Processing Systems. Pages 119--131. [BibTex]
• Nando de Freitas, Mahesan Niranjan, Andrew Gee and Arnaud Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation. Vol 12 No 4, pages 933-953. [BibTex]

• Nando de Freitas, Mahesan Niranjan and Andrew Gee. Hierarchical Bayesian models for regularisation in sequential learning. Neural Computation. Vol 12 No 4, pages 955-993. [BibTex]

 

 

1999

• PHD THESIS: Bayesian Methods for Neural Networks. Trinity College. University of Cambridge. 1999. .

• Christophe Andrieu, Nando de Freitas, Arnaud Doucet. Sequential MCMC for Bayesian Model Selection. IEEE Signal Processing Workshop on Higher Order Statistics. Ceasarea, Israel. [BibTex]