Selected publications

2012

  • 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.

  • 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

  • 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.

 

 

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.

  • Firas Hamze, Ziyu Wang and Nando de Freitas Self-Avoiding Random Dynamics on Integer Complex Systems. Technical Report arXiv:1111.5379v1

  • 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. [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

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

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

  • 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. [link]

    • 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

     

     

    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

     

     

    2001

    • 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]

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