Statistical Learning of Complex Data with Complex Distributions

Presentations

By investigators


Dale Schuurmans, "Unsupervised and semi-supervised machine learning" (6 lectures), Machine Learning Summer School, Canberra, Australia, February 2009.

Richard Sutton, "Core Learning Algorithms for Intrinsically Motivated Agents", IM-CLeVeR Workshop held in conjunction with the 9th Int. Conf. on Epigenetic Robotics, Nov 17, 2009, Venice, Italy.

Richard Sutton, "Rehabilitation Applications of Reinforcement Learning", Glenrose Rehabilitation Hospital, Nov 20, 2009, Edmonton.

Richard Sutton, "Toward Learning Human-Level Predictive Knowledge", Keynote at the Third Conference on Artificial General Intelligence, March 5, 2010, Lugano, Switzerland.

Yoshua Bengio, "Deep Learning Algorithms", Workshop on "Cognitive and neural models for automated processing of speech and text", July 9th, Ghent, Belgium.

Yoshua Bengio, "Understanding and Improving Deep Learning Algorithms", April 22nd 2010, CMU, Pittsburgh, PA, USA.

Yoshua Bengio, "Deep Learning for Speech and Language", NIPS'2009 Workshop on Deep Learning for Speech Recognition and Related Applications December 12, 2009, Whistler, BC, Canada.

Yoshua Bengio, "Experimental Investigations into Deep Architectures", CIFAR NCAP Meeting, December 6th, 2009, Vancouver, BC, Canada.

Yoshua Bengio, "Algorithmes d'apprentissage pour l'intelligence artificielle", Journée commémorative du RQCHP, 26 novembre 2009, Université de Montréal, Montréal, Qc, Canada.

Yoshua Bengio, "Learning Deep Hierarchies of Representations" September 23rd 2009, Google Research, Mountain View, California, USA.

Yoshua Bengio, "Learning Deep Feature Hierarchies" September 21st 2009, Stanford University, Stanford, California, USA.

Hugh Chipman, Data mining (1-day workshop), April 19, 2010, Statistical Society of Ottawa.

Hugh Chipman, Sequential optimization of a computer model: what's so special about Gaussian processes anyway?, May 26, 2010, SSC, Quebec City.

Hugh Chipman, Discussion of Nonparametric Profile Monitoring by Mixed Effects Modeling, August 3, 2010, Joint Statistical Meetings, Vancouver.

Hugh Chipman "Mining Transactional Network Data"
  • Statistical Society of Ottawa, April 20, 2010
  • Workshop on "Challenging problems in Statistical Learning", Paris, January 28, 2010
  • Simon Fraser Dept. of Statistics & ActSci,
  • February 12, 2010 Dalhousie School of Computer Science, February 18, 2010

Shai Ben-David,Invited Keynote talk at ECML-PKDD , Beld Slovenia, September 2009.

Shai Ben-David, Invited keynote talk at Israeli Workshop on Machine Learning, Haifa, Israel, March 2009.

Hugh Chipman, "Feature Selection with a Bayesian Ensemble Model", October 8, 2009, Fall Technical Conference, Indiannapolis, IN. (pdf)

Hugh Chipman, "Discussion of presentations by Jeremy Oakley and Brian Reich", August 4, 2009, Joint Statistical Meetings, Washington DC. (pdf)

Hugh Chipman, "Variable Selection via a Bayesian Ensemble", August 4, 2009, Joint Statistical Meetings, Washington DC. (pdf)

Hugh Chipman, "Statistical learning for networks", May 29, 2009, Spring Research Conference, Burnaby, BC. (pdf)

Hugh Chipman, "Statistical learning with trees",June 3 and October 16, 2009, (CRM-SSC lecture) SSC Annual Meeting, Vancouver, BC, June 3, 2009 and CRM, October 16, 2009. (pdf)

Yoshua Bengio, "Learning Deep Hierarchies of Representations", September 23rd 2009, Google Research, Mountain View, California, USA. (pdf) (mp4 video)

Yoshua Bengio, "Learning Deep Feature Hierarchies", September 21st 2009, Stanford University, Stanford, California, USA. (pdf)

Yoshua Bengio, "Learning Deep Architectures", August 6th, 2009, Toronto CIFAR Summer School 2009, Ontario, Canada. (pdf)

Yoshua Bengio, "On The Difficulty of Training Deep Architectures", July 8th 2009, Deep Learning Workshop, Gatsby Unit, UCL, London, U.K. (pdf)

Yoshua Bengio, "Learning Deep Architectures", July 7th 2009, Microsoft Research, Cambridge, U.K. (pdf)

Yoshua Bengio, "Learning Deep Architectures", UAI'2009, June 19th 2009, Montreal, Qc, Canada. (pdf)

Yoshua Bengio, "Tutorial: Learning Deep Architectures", ICML'2009 Workshop on Learning Feature Hierarchies, June 18th 2009, Montreal, Qc, Canada. (pdf)

Yoshua Bengio, "Learning Deep Architectures: a Stochastic Optimization Challenge", May 9th, 2009, CIFAR NCAP Workshop, Millcroft Inn, Ontario, Canada. (pdf)

Yoshua Bengio, "Learning Deep Architectures: a Stochastic Optimization Challenge", May 6th, 2009, Waterloo University, Waterloo, Ontario, Canada. (pdf)

Yoshua Bengio, "Bebe IA: de l'humain a la machine", Cegep de Valleyfield, 28th April, 2009, Valleyfield, Qc, Canada. (pdf)

Yoshua Bengio, "Curriculum Learning", Learning Workshop (selected for oral presentation), April 16th 2009, Clearwater, Florida, USA. (pdf)(similar talk given at ICML2009 pdf)

Yoshua Bengio, "Statistical Machine Learning for Target Classification from Acoustic Signature", Army Research Lab, January 15th, 2009, Adelphi, Maryland, USA.

Yoshua Bengio, "Learning Algorithms for Deep Architectures", NIPS'2008 Workshop on Machine Learning Meets Human Learning, December 12th, 2008, Whistler, B.C., Canada. (pdf)

Yoshua Bengio, "On the difficulty of training deep neural nets and the effect of unsupervised pre-training", CIFAR NCAP Workshop, December 7th, 2008, Vancouver, B.C., Canada.

Yoshua Bengio, "Machine learning and the curse of highly-variable functions", November 14th, 2008, Montreal Music and Machine Learning Workshop 2008, Montreal, Qc, Canada. (pdf)

Pascal Vincent, "Deep Learning with Denoising Autoencoders", Présentation orale au Learning Workshop 2008 3 avril 2008, Snowbird, Utah, USA.

Pascal Vincent, "On the Relevance of Neural Networks for Data Mining: How They Work, Why They Matter, and the Promises of Current Research", Conférencier invité à l'école d'été du GERAD: GERAD 2008 Summer School on Advances in Data Mining, 8 mai 2008, Montréal, QC.

Pascal Vincent, "Extracting and Composing Robust Features with Denoising Autoencoders", International Conference on Machine Learning 2008, Présentation orale et poster, 8 juillet 2008, Helsinki, Finlande.

Pascal Vincent, "Autoencoders, denoising autoencoders, and learning deep networks", Présentation orale à CIfAR (Canadian Institute for Advanced Research) Summer School on Learning and Vision in Biology and Engineering, 6 août 2008, Université de Toronto, ON.

Yoshua Bengio, "Bebe IA: de l'humain a la machine", University of Montreal, Oct 7th, 2008 Montreal, Canada. (pdf)

Yoshua Bengio, "Learning Deep Representations", DARPA Deep Learning Workshop, Sept 25th, 2008, Virginia, USA. (pdf)

Yoshua Bengio, "Learning deep architectures for AI", University of Siena, May 29th, Siena, Italy, 2008. (pdf)

Yoshua Bengio, "Learning deep architectures for AI", University of Florence, May 26th, Florence, Italy, 2008. (pdf)

Yoshua Bengio, "Optimizing Deep Architectures" NIPS Satellite Meeting on Deep Learning December 6th, Vancouver, BC, 2007.

Richard Sutton, "Mind and Time: A View of Constructivist Reinforcement Learning", invited plenary talk at the European Workshop on Reinforcement Learning, Lille, France, 2008.

Richard Sutton, "Stimulus Representation in Temporal-difference Models of the Dopamine System", Computation and Neural Systems seminar series on the California Institute of Technology, 2007.

Shai Ben-David, "the sample complexity cost of choosing a kernel", NIPS08 workshop on "Automatic Kernel Selection", December, 2008.

Shai Ben-David, "Incorporating prior knowledge and empirical evidence in the choice of a learning bias", NIPS’08 workshop on Empirically Defined Hypothesis Spaces tutorial, December 2008.

Shai Ben-David, "Learning when the training data and the test data distributions are not the same", U. Alberta, Edmonton, colloquium, October, 2008.

Shai Ben-David, "An axiomatic basis for clustering – overcoming Kleinberg’s impossibility result", TTI Chicago colloquium, April 2008.

Shai Ben-David, "The theory of clustering and its practical implications", U. Alberta, Edmonton, colloquium , April 2008.

Shai Ben-David, "The effect of boundary data density on clustering stability", COLT 2008, Helsinki, August, 2008.

Shai Ben-David, "Theoretical Foundations of Clustering", Machine Learning Summer School (MLSS08) Ile De Re, France, September 2008.

Shai Ben-David, "The theory of clustering and its practical implications", Distinguished lecture series. Queens University, March 2008.

Hugh Chipman, "Variable selection via a Bayesian ensemble", Joint Statistical Meetings, Denver, CO, August 7, 2008.

Mu Zhu, "Kernels and ensembles", seminar, Fred Hutchinson Cancer Research Center, Seattle, November 2007.

Hugh Chipman, "Supervised learning for graph-structured data", UBC Statistics department, June 27, 2008.

Hugh Chipman, "Supervised classification for graph-structured data, using latent social networks", MITACS/MASCOS Joint Workshop on Fusion, Mining and Security for Networks, June 19, 2008.

Hugh Chipman, "Statistical Learning via Bayesian Computation and Other Methods", Bluenose Numerical Analysis Day, June 13, 2008, Dalhousie University.

Hugh Chipman, "Tips for preparing an NSERC Discovery Grant", SSC annual meeting, Ottawa, May 26, 2008.

Hugh Chipman, "Mining Network Data", Poster at SSC annual meeting, Ottawa, May 28/2008

Hugh Chipman, "Statistical learning from complex data", University of Waterloo, March 19, 2008.

Hugh Chipman, "Supervised learning via Bayesian Computation", Coast to Coast Mathematics Seminar, November 6, 2007

Hugh Chipman, "Statistical learning and virtual screening in drug discovery", McGill University, October 24, 2007

Pascal Vincent, "Bayesian Ensemble Active Learning"(slide), UdeM-McGill-MITACS seminar, Montréal, March 25, 2008.

Yoshua Bengio, "Optimizing Deep Architectures"(video part1, part2), Deep Learning Workshop: Foundations and Future Directions, Hyatt hotel, Vancouver, Canada, December 6th, 2007.

Hugh Chipman, "Bayesian Ensemble Active Learning", Joint Statistical Meetings, Salt Lake City, August 29, 2007.

Hugh Chipman, "Supervised Learning via Bayesian Computation", Statistical Science: Present position and future prospects, University of Waterloo, May 30, 2007.

Hugh Chipman, "BART; Bayesian Additive Regression Trees", University of Washington, April 16, 2007.

Hugh Chipman, "Learning from data: statistical ideas and algorithms", Acadia Science Cafe, March 5, 2007.

Hugh Chipman, "Monitoring functional data: mixed effects and high-dimensional clustering", CRM-ISM-Gerad Colloquium de Statistique February 23, 2007.

Hugh Chipman, "Bayesian Ensemble Learning", poster, NIPS 2006, Dec 8, 2006.

Hugh Chipman, "Introduction to R", NPCDS/MITACS/CRM Spring School on Statistical and Machine Learning, University of Montreal, May 23-27, 2006.

Hugh Chipman, "R lab on statistical learning and resampling methods", NPCDS/MITACS/CRM Spring School on Statistical and Machine Learning, University of Montreal, May 23-27, 2006.

Hugh Chipman, "A neural net example", NPCDS/MITACS/CRM Spring School on Statistical and Machine Learning, University of Montreal, May 23-27, 2006.

Hugh Chipman, "R lab on neural nets", NPCDS/MITACS/CRM Spring School on Statistical and Machine Learning, University of Montreal, May 23-27, 2006.

Hugh Chipman, "Bayes 101: An introduction to Bayesian Methods", Biology Department, Acadia University, April 25, 2006.

Hugh Chipman, "Research in Mathematical Modelling", Acadia CRC event, January 20, 2006.

Hugh Chipman, "Statistical Modelling in Medical Compliance Problems", Statistical Consulting Centre workshop on compliance, December 9, 2005.

Richard Sutton, "What's Wrong with Reinforcement Learning", workshop: Towards a New Reinforcement Learning, Twentieth Annual Conference on Neural Information Processing Systems (NIPS-2006), December 8, 2006.

Richard Sutton, "Grounding Artificial Cognition", workshop: Grounding Perception, Knowledge and Cognition in Sensori-Motor Experience, Twentieth Annual Conference on Neural Information Processing Systems (NIPS-2006), December 8, 2006.

Dale Schuurmans, "Convex Relaxations of EM Statistical Machine Learning Laboratory", National ICT Australia, May 22, 2007

Mu Zhu, "Darwinian evolution in parallel universes" Nov 06, 2007: INFORMS annual meeting (Seattle, USA).

Mu Zhu, "LAGO: Efficient RBFnets for rare target detection" Jun 08, 2006: Joint Research Conference: Statistics in Quality, Industry and Technology (Knoxville, TN, USA).

Mu Zhu, "Predicting Rehabilitation Potential: KNN versus ADLCAP" May 24, 2006: 17th IASTED International Conference on Modelling and Simulation (Montreal, QC, Canada).

Mu Zhu, "LAGO: Efficient RBFnets for rare target detection" Apr 20, 2006: Statistical Society of Ottawa Symposium: New Frontiers in Statistics (Ottawa, ON, Canada).

Mu Zhu, "A controversy in quadratic discriminant analysis" Mar 24, 2006: University of Waterloo, Department of Statistics & Actuarial Science.

Yoshua Bengio, "Deep architectures for Baby AI", CIAR Summer School August 7-11th, 2007, University of Toronto, Toronto, ON.

Yoshua Bengio, "Learning long-term dependencies with recurrent networks", CIAR NCAP Workshop on sequential data June 14th, 2007, Millcroft Inn, Alton, ON.

Yoshua Bengio, "Non-Local and Deep Representations for AI", NIPS 2006 Workshop on Novel Applications of Dimensionality Reduction, December 9th, 2006, Whistler, BC.

Pascal Vincent, "The PLearn Machine Learning Library", Neural Information Processing Systems 2006, Workshop on Machine Learning Open Source Software, 9 décembre 2006, Whistler, BC.

Pascal Vincent, Talk about the poker match between 2 of the best humans players and computer, Invited to the radio show "Les années lumiére" on Radio-Canada with Sophie-Andrée Blondin, July 29 2007.

Yoshua Bengio, "Greedy Layer-Wise Training of Deep Networks", UdeM-McGill-MITACS seminar, november 21 2006.

Bengio, Y., Facing Non-Convex Optimization to Scale Machine Learning to AI, Workshop on Data Mining and Mathematical Programming, Université de Montréal, October 2006

Chipman, H.A., George, E.I. and McCulloch, R.E., "Sequential design for drug discovery", DEMA workshop, Southampton, UK, Sept 9, 2006.

Chipman, H.A., "Mining transactional data on networks", CAIMS/MITACS annual meeting, York University, June 19, 2006.

Bengio, Y. and Lamblin, P., "Highlights of Hinton's Contrastive Divergence Pre-NIPS Workshop", UdeM-McGill-MITACS seminar, Université de Montréal, January 2006.

Zhu, M., "Kernels and ensembles: A workshop on statistical machine learning & data mining",
Sydney, Sep 27,2006.
Adelaide, Oct 24, 2006.

Zhu, M., "Darwinian evolution in parallel universes", Australia, 2006.
SSAI New South Wales Local Branch Meeting, Sep 19, 2006.
University of Newcastle, School of Mathematical & Physical Sciences, Sep 20, 2006.
Queensland University of Technology, School of Mathematical Sciences, Oct 04, 2006.
SSAI Western Australia Local Branch Meeting, Oct 10, 2006.
University of Melbourne, Department of Mathematics and Statistics, Oct 16, 2006.
SSAI Canberra Local Branch Meeting, Oct 31, 2006.

Zhu, M., "LAGO: Efficient RBFnets for Rare Target Detection",
University of New South Wales, School of Mathematics and Statistics, September 22, 2006.
Macquarie University, Department of Statistics, September 26, 2006.
SSAI Queensland Local Branch Meeting, October 03, 2006.
CSIRO – Perth, October 10, 2006.
CSIRO – Clayton, Melbourne, October 12, 2006.
National ICT Australia, Statistical Machine Learning Group, October 27, 2006.
Australian National University, Mathematical Science Institute, October 30, 2006.
Australian Taxation Office, Canberra, November 02, 2006.

Zhu, M., "Discriminant analysis and environmental ecology",
Queensland University of Technology, School of Mathematical Sciences, October 05, 2006.
CSIRO – Cleveland, Brisbane, October 06, 2006.
SSAI South Au\stralia Local Branch Meeting, October 25, 2006.

Zhu, M., "The support vector machine: A tutorial", National Australia Bank, Melbourne, October 19, 2006.

Sutton, R., "Empirical Artificial Intelligence", UdeM-McGill-MITACS seminar, McGill University, November 2005.

Bengio, Y., "Convolutional Neural Networks and Vision Applications", UdeM-McGill-MITACS seminar, Université de Montréal, November 2005.

Chipman, H., "Opportunities in Parallel Statistical Computing", Fields Workshop on Data Mining closed research session, November 13, 2005.

Schuurmans, D., "Convex Hidden Markov Models", Cornell University, November 4th, 2005.

Ramsay, J. O., "From data to differential equations," invited address at the International Association for Statistical Computering 3rd World Conference on Computational Statistics and Data Analysis, Limassol, Cyprus, October 28-31, 2005.

Chipman, H., "Opportunities in Parallel Statistical Computing", AARMS workshop, Acadia University, October 23, 2005.

Ramsay, J. O., "Workshop on functional data analysis, Insightful Corporation", Seattle, Wash., September 15-16, 2005.

Chipman, H., "Pattern Discovery in Massive Social Networks", SAMSI NDHS workshop, September 14, 2005.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", Stanford University, Department of Statistics, Seminar, September 2005.

Schuurmans, D., "Convex Hidden Markov Models", Conference of Institute for Biocomplexity and Informatics, Banff Centre, August 29th, 2005.

Léger, C., "Selecting Likelihood Weights by the Bootstrap", MITACS Workshop on Statistical Learning of Complex Data from Complex Distributions, August 26th, 2005, Montreal.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", MITACS Workshop on Statistical Learning of Complex Data from Complex Distributions, August 26th, 2005, Montreal.

Schuurmans, D., "Discriminative, Unsupervised, Convex Learning", MITACS Workshop on Statistical Learning of Complex Data from Complex Distributions, August 26th, 2005, Montreal.

Bengio, Y., "Neural Networks, Convexity, Kernels and Curses", MITACS Workshop on Statistical Learning of Complex Data from Complex Distributions, August 26th, 2005, Montreal.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", Joint Statistical Meetings (Minneapolis, MN), August 2005

Bengio, Y., "Reassuring and Troubling Views on Graph-Based Semi-Supervised Learning", August 7th, 2005, Bonn, ICML'2005 Workshop on Partially Supervised Learning.

Sutton, R., "Predictive Representations of State and Knowledge", 2005 International Conference on Machine Learning, Bonn, Germany, August 7th, 2005.

Bengio, Y., "Neural Networks, Convexity, Kernels and Curses", August 5th, 2005, Hebrew University, Jerusalem, Israel.

Ramsay, J. O., "From data to differential equations," invited plenary address at the 2005 International Meeting of the Psychometric Society, Tilburg, Netherlands, July 4-9, 2005.

Delalleau, O., Bengio, Y. and Le Roux, N., "Graph-based semi-supervised learning", Presented at CIAR workshop on manifold learning, Montreal, Canada, 2005.

Delalleau, O., Bengio, Y. and Le Roux, N., "Graph-based semi-supervised learning", MITACS-LISA seminar, Université de Montréal, June 2005.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", Harvard University, Department of Statistics, Seminar, June 2005

Bengio, Y., "The Curse of Dimensionality for Local Learning", Microsoft Research, May 25th, 2005, Redmond, Washington.

Sutton, R., "Grounding Knowledge in Subjective Experience", invited talk at the Second Cognitive Systems Conference, Arlington, VA, May 19, 2005.

Sutton, R., "Experience-Oriented Artificial Intelligence", distinguished lecture series of the Department of Mathematics and Computer Science at Lethbridge University, May 11, 2005.

Chipman, H., "Bayesian Additive Regression Trees", PAMI group, University of Waterloo, April 26, 2005.

Sutton, R., "Robots that Learn", Lethbridge University, April 18, 2005.

Sutton, R., "Experience-Oriented Artificial Intelligence", distinguished lecture series of the Department of Mathematics and Computer Science at Lethbridge University, April 18, 2005.

Sutton, R., "Robots that Learn", Odyssium, Edmonton, Alberta, April 16, 2005.

Bengio, Y., "The Curse of Dimensionality for Local Kernel Machines", The Learning Workshop, April 7th, 2005, Snowbird, Utah.

Bengio. Y., "Curse of Dimensionality: Local versus Non-Local Learning", CIAR Manifold learning workshop, April 25th, 2005, Montreal.

Bengio, Y., "Curse of Dimensionality: Local versus Non-Local Learning", MITACS-LISA seminar, Université de Montréal, April 2005.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", Dalhousie University, Department of Mathematics and Statistics, Seminar, February 2005

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", Acadia University, Department of Mathematics and Statistics, Seminar, February 2005.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", University of Toronto, Department of Statistics, Seminar, January 2005

Chipman, H., "Data analysis using R", Statistical Consulting Centre workshop, Acadia University, January 2005.

Bengio, Y., "Non-Local Learning of Geometric Invariants and Beyond", CIAR Neural Computation Workshop, December 12th, 2004, Vancouver.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", University of Waterloo, Department of Statistics and Actuarial Science, Seminar, December 2004

Chipman, H., "Bayesian Additive Regression Trees", CIMAT, Mexico, November 29 and December 1, 2004.

Chipman, H., "Bayesian Additive Regression Trees", Dalhousie University seminar, November 18, 2004.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", University of British Columbia, Department of Statistics, Seminar, November 2004.

Zhu, M., "LAGO: A computationally efficient approach for statistical detection", McMaster University, Department of Mathematics and Statistics, Seminar, November 2004.

Ramsay, J. O., "From data to differential equations," invited plenary address at the XXVIII Congreso Nacional de Estadistica e Investigacion Operativa in Cadiz, Spain, October 25, 2004.

Bengio, Y., "Statistical Learning from High Dimensional and Complex Data: Not a Lost Cause", Fields Institute Data Mining Workshop, October 2004.

Ramsay, J. O., "From data to differential equations," invited plenary address at the Compstat 2004 Meeting in Prague, August 23-27, 2004.

Bengio, Y., "Local and Non-Local Manifold Learning", University of Toronto, August 2004.

Bengio, Y., "Local and Non-Local Manifold Learning", MIT, August 2004.
Ramsay, J. O., "Estimating differential equations," invited plenary opening address at the Meeting of the International Federation of Classification Societies, Chicago, July 15-18, 2004.

Bengio, Y., "Learning the Density Structure of High-Dimensional Data", Joint Canada-France Meeting of the Mathematical Sciences, Toulouse, 2004.


Ramsay, J. O., "Estimating differential equations," presented at the Canada-France Meeting of Mathematics, Toulouse, France, July 12-15, 2004.

Bengio, Y., "Learning the Density Structure of High-Dimensional Data", Computer Discovery Conference, Montréal, Québec, Canada, June 2004.

Bengio, Y. "Variations that Summarize Similarities between Objects", NIPS'2003 post-conference workshops, Whistler, British-Columbia, Canada, December 2003.

Bengio, Y. "Learning Internal Representations by Capturing the Principal Factors of Variations that Summarize Similarity between Objects", Canadian Institute for Advanced Reserach, Vancouver, British-Columbia, Canada, December 2003.

Bengio, Y. "A Quick Tour of Some Supervised Machine Learning Algorithms", CLSP Workshop 2003, Johns Hopkins University, July 10, 2003.

Bengio, Y. "Learning Eigenfunctions Generalizes Spectral Clustering, ISOMAP, Laplacian Eigenmaps and Others", Workshop on Advances in Machine Learning, Montréal, Québec, Canada, June 9, 2003.

Bengio, Y. and Grandvalet, Y. "Estimators of Variance for K-Fold Cross-Validation", Workshop on Advances in Machine Learning, Montréal, Québec, Canada, June 10, 2003.

Bengio, Y. "Learning Eigenfunctions to Model High-Dimensional Data", Statistical Society of Canada, McGill University, Montréal, Québec, Canada, April, 2003.

Bengio, Y., Vincent, P. and Paiement, J.-F. "Learning Eigenfunctions: Links with Spectral Clustering and Kernel PCA", Learning'2003 conference, Snowbird, Utah, USA, April 2, 2003.

Bengio, Y. "No unbiased Estimator of Variance for K-Fold Cross-Validation", NIPS'2002 post-conference workshops, Whistler, British-Columbia, Canada, December 2002.

Bengio, Y. "Faking unlabeled date for geometric regularization", IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland, September 2002.

Bengio, Y. "On the role of machine learning in language modeling", CIAR Workshop, Toronto, Canada, July 2002.

Bengio, Y. "Facing the curse of dimensionality in statistical language modeling", Johns Hopkins CLSP Workshop, July 2002, Baltimore, MA, USA.

Bengio, Y. "Distributed representations for statistical language modeling", Université Paris 6, Paris, France, September 2002.

Bengio, Y. "Statistical learning from high-dimensional data", MITACS board meeting, Bell University Laboratories, Montréal, Qc, Canada, October 2002.

Bengio, Y. , Ichiro Takeuchi, Takafumi Kanamori "The challenge of  non-linear regression on large datasets with asymmetric heavy tails", Joint Statistical Meeting, New-York, NY, USA, August 2002.

Ducharme R., Bengio, Y. "The name mining project", Bell Canada (talk invited by the Consumer Modelling and Segmentation department), Montréal, Qc, Canada, September 2002.

Bengio, Y. "Statistical learning from high-dimensional data", MITACS Annual General Meeting, Vancouver, BC, Canada, May 2002.

Bengio, Y. "Foundations and applications of statistical learning algorithms", CIRANO, Montréal, Qc, Canada, May 2002.

Collobert, R., Bengio, Y. "Magic Mix", Learning'2002 conference, April 2002, Snowbird, Utah, USA.

Bengio, Y. "Beating the Curse of Dimensionality in Probabilistic Models of Language", Statistics 2001, July 6-8, 2001, Montréal, Quebec, Canada.

Bengio, Y. "Data Mining for Bell Canada", Bell University Laboratories, Montréal, Qc, Canada, March 2001.

Schuurmans, D. "Topics in Unsupervised Learning: Discovering Manifold of Subparts", Workshop on Advances in Machine Learning, Montréal, Québec, Canada, June 10, 2003.

Schuurmans, D. (2002) "Regularized greedy importance sampling", CIAR Workshop on Machine Learning

Schuurmans, D. (2002) "Local search strategies for SAT", Waterloo Formal Methods Colloquium, Department of Computer and Electrical Engineering, University of Waterloo

Schuurmans, D. (2001) "Metric-based model selection and regularization", MITACS Theme Meeting, Universite de Montréal

Schuurmans, D. (2001) "The exponentiated subgradient algorithm for heuristic Boolean programming", Department of Computing Science, University of Alberta

Schuurmans, D. (2001) "Direct value-approximation for factored MDPs", Department of Computing Science, University of Toronto

Schuurmans, D. (2001) "Monte Carlo inference via greedy importance sampling", School of Information Technology and Engineering, University of Ottawa

Schuurmans, D. (2001) "Computational learning: Limits and new techniques", Cognitive Science Forum, Department of Philosophy, University of Waterloo

Schuurmans, D. (2000) "Monte Carlo inference via greedy importance sampling", at Carnegie Mellon University and WhizBang! Labs, Pittsburgh.

Schuurmans, D. (2000) "Metric-based methods for adaptive model selection and regularization", at Carnegie Mellon University.

Zhu, M., Chipman, H. and Su, W., "An adaptative method for statistical detection with applications to drug discovery", talks given by Mu Zhu based on doctoral research by Chipman and Su, given at The National Program on Complex Data Structures, Workshop on Data Mining Methodology and Applications, Fields Institute, Toronto, October 28, 2004.

Zhu, M., Chipman, H. and Su, W., "An adaptative method for statistical detection with applications to drug discovery", talks given by Mu Zhu based on doctoral research by Chipman and Su, given at University of Guelph, Department of Statistics, October 21, 2004.

Zhu, M., Chipman, H. and Su, W., "An adaptative method for statistical detection with applications to drug discovery", talks given by Mu Zhu based on doctoral research by Chipman and Su, given at Joint Statistical Meetings, Toronto, August 9, 2004.

Zhu, M., Chipman, H. and Su, W., "An adaptative method for statistical detection with applications to drug discovery", talks given by Mu Zhu based on doctoral research by Chipman and Su, given at University of Waterloo, Department of Statistics and Actuarial Science, July 22, 2004.

Zhu, M., Chipman, H. and Su, W., "An adaptative method for statistical detection with applications to drug discovery", talks given by Mu Zhu based on doctoral research by Chipman and Su, given at The Mathematics of Information Technology and Complex Systems (MITACS) 5th Annual Conference, Halifax, Nova Scotia, June 10, 2004.

Chipman, H., R. McCulloch and E. George, "Data Mining and Statistical Learning", Acadia Symposium on Modelling and Computation, 2004.

Chipman, H. and Y. Yuan, "Interpretability of trees from high-throughput screening data", Joint Statistical Meeting, Toronto, August 11, 2004.

Chipman, H., Wang, Y. and Y. Yuan, "Statistical Learning in Drug Discovery", University of Guelph, March 26, 2004.

Chipman, H., Wang, Y., X. Wang and Y. Yuan, "Some research in data mining", SAMSI mid-year workshop on data mining and machine learning, Research Triangle Park, NC, February 4, 2004.

Chipman, H., "Using Data Mining to Uncover Rare, Valuable Outcomes", MITACS Quebec Interchange, Montréal, November 13, 2003.

Chipman, H., R. McCulloch and E. George, "Bayesian Additive Regression Trees", University of Waterloo AI Group, November 7, 2003.

Chipman, H., "Statistical Learning and Data Mining", roundtable luncheon, 2002 joint statistical meeting.

Chipman, H., "Learning Treed Generalized Linear Models", 2002 joint statistical meeting, and 2002 Interface meeting, and Stanford University 2002, and University of Chicago 2002.

Chipman, H., "Hybrid Hierarchical Clustering With Applications To Microarray Data", 2002 Statistical Society of Canada meeting and 2002 Institute of Mathematical Statistics meeting.

Chipman, H., "Data Mining", a half-day workshop at the 2002 International Indian Statistical Association meeting.

Chipman, H., "Mining Functional Process Data", 2002 Spring Research Conference on Statistics in Industry and Technology, and 2002 International Indian Statistical Association meeting

Chipman, H.,  "Regression and Classification With Tree Models: Forests, Hybrids, and Other New Methods", University of Michigan, 2002.

Chipman, H., "Monitoring production with immense datasets", Guest speaker, Aouthern Ontario Graduate student seminar day, 2001

Chipman, H., "Managing Multiple Models", Eighth International Workshop on Artificial Intelligence and Statistics, 2001, and Joint Statistical Meetings, 2001.

Chipman, H., "Classification and Regression Trees", at Predicting health and health services: data, methods and models, ICES, 2001.

Chipman, H., "Optimal Designs for Model Selection", Valencia International Meetings on Bayesian Statistics, 2002

Chipman, H., "Statistical methods for high throughput screening data",  MITACS IT-theme meeting, 2001
Welch, W.J. "Statistical Methods for Deterministic Biomathematical Models", 53rd Session of the International Statistical Institute, Seoul, S. Korea, August 29, 2001.

Welch, W.J. "Kriging Estimation Within a Decision-Making Framework  for Evaluating Process Technologies in a Wastewater Treatment System", INFORMS, San Antonio, Texas, November 6, 2000.

By students


James Bergstra and Yoshua Bengio, "GPU Programming with Theano", SHARCNET Research Day, 2010.

James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Joseph Turian, Guillaume Desjardins, Razvan Pascanu, Dumitru Erhan, Olivier Delalleau and Yoshua Bengio, "Deep Learning on GPUs with Theano", USA, Snowbird 2010.
Yoshua Bengio, Hugo Larochelle and Joseph Turian, "Deep Woods", USA, Snowbird 2008.

M. Mahdi Shafiei and Hugh Chipman, "Mixed-Membership Stochastic Block-Models for Transactional Data", Workshop on Analyzing Networks and Learning with Graphs, Neural Information Processing Systems (NIPS), Whistler, BC, December 11, 2009.

Dumitru Erhan,Aaron Courville, Yoshua Bengio and Pascal Vincent "Visualizing Higher Layer Features of a Deep Network",
Spotlight presentation and poster at the ICML 2009 Workshop on Learning Feature Hierarchies, Montréal, Canada.

Aaron Courville, Dumitru Erhan, Pascal Vincent, and Yoshua Bengio "Sparse Covariance Coding",
Poster presentation at the Learning Workshop ("Snowbird"), Snowbird, Utah, April 2008.

Elliot Ludvig, "Stimulus Representation in Reinforcement-learning models of Brain and Behaviour",
invited talks at the Gatsby Computational Neuroscience Unit, London, England, Mar 2008
and at the Riken Brain Science Institute, Tokyo, Japan, Oct 2008.

David Silver, "Achieving Master Level Play at 9x9 Go", invited talk at IBM Watson, Hawthorne, New York, USA, 2008; paper presented at AAAI, Chicago, USA, 2008.

David Silver, "A Unified Framework for Sample-Based Learning and Sample-Based Search",
invited talks at Microsoft Research, Cambridge, UK, 2008
and at the Gatsby Computational Neuroscience Unit, London, UK, 2008.

David Silver, "Sample-based Learning and Search from Permanent and Transient Memories", paper presented at ICML, Helsinki, Finland, 2008 and at EWRL, Lille, France, 2008.

Margarita Ackerman, "Measures of Clustering Quality: A Working Set of Axioms for Clustering" NIPS 2008. December 2008.

Tyler Lu, "On the sample complexity of semi-supervised learning", COLT 2008, Helsinki, August, 2008. Joint work with other students David Pal.

James Bergstra, "Image Classification with Higher-Order Neural Models"(slide), UdeM-McGill-MITACS seminar, March 25, 2008.

Yoshua Bengio, Hugo Larochelle and Joseph Turian, "Deep Woods", USA, Snowbird 2008.

Mohammad Ghavamzadeh, "Bayesian Policy Gradient Algorithms", workshop: Towards a New Reinforcement Learning, Twentieth Annual Conference on Neural Information Processing Systems (NIPS-2006), December 8, 2006.

Hugo Larochelle, Dumitru Erhan and Yoshua Bengio, "Generalization to a zero-data task: an empirical study", Snowbird 2007, 20 march 2007.

Nicolas Le Roux, Pierre-Antoine Manzagol and Yoshua Bengio, "Topmoumoute Online Natural Gradient Algorithm", Snowbird 2007, 20 march 2007.

Nicolas Le Roux and Yoshua Bengio, "Continuous Neural Networks", AISTAT 2007, 23 march 2007.

Hugo Larochelle, "An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation", ICML 2007, june 2007.

Pascal Lamblin, "Learning the 2-D Topology of Images", UdeM-McGill-MITACS seminar, Université de Montréal, 19 october 2007.

Hugo Larochelle, "An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation", UdeM-McGill-MITACS seminar, 13 june 2007.

Jérôme Louradour, "Sequence kernels for Speaker Verification using Support Vector Machines", UdeM-McGill-MITACS seminar, 18 april 2007.

Nicolas Le Roux, "Topmoumoute Online Natural Gradient Algorithm", UdeM-McGill-MITACS seminar, 13 mars 2007.

Dumitru Erhan, "Bayesian Ranking using Factor Graphs and Expectation Propagation", UdeM-McGill-MITACS seminar, 7 november 2006.

Courville, A., "Dirichlet processes : interpretations, inference and extensions", UdeM-McGill-MITACS seminar, Université de Montréal, October 2006.

Erhan, D., "Collaborative Filtering for Drug Discovery", UdeM-McGill-MITACS seminar, Université de Montréal, May 2006.

Le Roux, N., "Continuous Neural Networks", UdeM-McGill-MITACS seminar, Université de Montréal, April 2006.

Lamblin, P., "Discriminant Mixture of 3D Molecular Surface Models", UdeM-McGill-MITACS seminar, Université de Montréal, April 2006.

Erhan, D., "Collaborative filtering on a family of biological targets", Presented at the 230th ACS National Meeting, in Washington, DC, 2005.

Popovici, D., "Echo State Networks: a Tutorial", UdeM-McGill-MITACS seminar, Université de Montréal, November 2005.

Chapados, N., "The K-Shortest-Paths Approach to Approximate Dynamic Programming", UdeM-McGill-MITACS seminar, Université de Montréal, September 2005.

Carreau, J., "A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tailed Data", UdeM-McGill-MITACS seminar, Université de Montréal, September 2005.

Courville, A., "A Bayesian account of animal learning", MITACS-LISA seminar, Université de Montréal, April 2005.

Le Roux, N., "La transformée de Fourier pour les nuls", MITACS-LISA seminar, Université de Montréal, March 2005.

Y. Yuan, Chipman, H. and Welch, W.J., "Averaging methods for high-throughput screening data", Joint Statistical Meeting, Toronto, August 9, 2004.

Vincent, P. and Bengio, Y., "Density-Sensitive Metrics and Kernels", Workshop on Advances in Machine Learning, Montréal, Québec, Canada, June 10, 2003.

Feng, J., Wang, Y., Lurati L., Yuan S., Robinson T., and Ouyang H., "Predictive Toxicology: Mixed Co-convolution", presented at Industrial Mathematics Modeling Workshop for Graduate Students at North Carolina State University, 2002.

Hughes-Oliver, J., Welch, W.J. and Young, S, "1-Day Workshop on Statistical Aspects of High Throughput Screening", National Institute of Statistical Sciences, Research Triangle Park, NC, October 25, 2002

Wang, Y., Chipman, H., and Welch, W.J., "Flexible Modelling of High Throughput Screening Data", presented by Welch, W.J. at the Spring Research Conference on Statistics in Industry and Technology, Ann Arbor, Michigan, May 21, 2002

Wang, Y., Chipman, H., and Welch, W.J., "High Throughput Screening Data for Drug Discovery", presented by Welch, W.J. at the University of British Columbia, July 18, 2002.

Wang, Y., Chipman, H., and Welch, W.J., "Flexible Modeling of High Throughput Screening Data", presented by Yuanyuan Wang at 2002 Statistical Society of Canada Annual Meeting, Hamilton, June 2002

Wang, Y., Chipman, H., and Welch, W.J., "Flexible Modeling of High Throughput Screening Data", presented by Yuanyuan Wang at the Design and Analysis of Experiments 1 Workshop, Vancouver, July 2002

Wang, Y., Chipman, H., and Welch, W.J., "Comparison of Statistical Methods for High Throughput Screening Data", presented by Yuanyuan Wang at the 24th Annual Midwest Biopharmaceutical Statistics Workshop, Muncie, IN, May 2001

Wang, Y., Chipman, H., and Welch, W.J., "Comparison of Statistical Methods for High Throughput Screening Data", presented by Yuanyuan Wang at the Statistical Society of Canada Annual Meeting, Vancouver, May 2001