Publications
- Refereed contributions
- Non-refereed contributions
Refereed contributions
Articles in refereed publications
Published or accepted:Chipman, H., George, E., Lemp, J. and McCulloch, R. (2010) Bayesian flexible modelling of trip durations, Transportation Research Part B, 44, 686-698.
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio and Pierre-Antoine Manzagol (2010). Stacked Denoising Autoencoders: learning useful representations in a deep network with a local denoising criterion, in: JMLR.
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jenn Wortman (2010). A Theory of Learning from Different Domains. Machine Learning 79(1-2): 151-175.
Hugo Larochelle, Yoshua Bengio and Joseph Turian (2010). Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest, in: Neural Computation, Vol. 22, No. 9.
Yoshua Bengio, Olivier Delalleau and Clarence Simard (2010). Decision Trees do not Generalize to New Variations, in: Computational Intelligence.
Nicolas Le Roux and Yoshua Bengio (2010). Deep Belief Networks are Compact Universal Approximators, in: Neural Computation, Vol. 22, No. 8, 2192-2207.
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent and Samy Bengio (2010). Why Does Unsupervised Pre-training Help Deep Learning?, in: Journal of Machine Learning Research, Vol. 11, 625-660.
James Bergstra, Yoshua Bengio and Jerome Louradour (2010). Suitability of V1 Energy Models for Object Classification, in: Neural Computation.
Chipman, H., MacKay, R.J. and Steiner, S (2010). Discussion of NonparametricProfile Monitoring By Mixed Effects Modeling, by Qiu, Zou and Wang, Technometrics, August, Vol. 52, No. 3: 280–283.
Chipman, H., George, E. and McCulloch, R. (2010) BART: Bayesian Additive Regression Trees, in Annals of Applied Statistics, 4, 266-298.
Shai Ben-David (2009). Theory-Practice Interplay in Machine Learning - Emerging Theoretical Challenges. ECML/PKDD (1).
Wu, L., Chipman, H. A., Bull, S. B., Briollais, L., and Wang, K. (2009), A Bayesian segmentation approach to ascertain copy number variations at the population level, Bioinformatics, 25, 1669-1679.
Julie Carreau and Yoshua Bengio (2009). A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distribution, in: IEEE Transactions on Neural Networks, 20:7, 1087-1101.
E. J. Kehoe, K. N. Olsen, E. A. Ludvig, R. S. Sutton (2009). Scalar Timing Varies with Response Magnitude in Classical Conditioning of the Nictitating Membrane Response of the Rabbit (Oryctolagus cuniculus), Behavioral Neuroscience 123, pp. 212-217.
E. A. Ludvig, R. S. Sutton, E. J. Kehoe (2008). Stimulus Representation and the Timing of Reward-prediction Errors in Models of the Dopamine System, Neural computation, 20, 3034-3054, 2008.
Kehoe, E. J., Ludvig, E. A., & Sutton, R. S (2009). Magnitude and timing of CRs in delay and trace classical conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus). Accepted to Behavioral Neuroscience on July 17. (in press)
François Rivest, John Kalaska and Yoshua Bengio (2009). Alternative Time Representations in Dopamine Models, in: Journal of Computational Neuroscience.
Charles Dugas, Yoshua Bengio, Francois Belisle, Claude Nadeau and Rene Garcia (2009). Incorporating Functional Knowledge in Neural Networks, in: The Journal of Machine Learning Research, 10(1239--1262).
Yoshua Bengio and Olivier Delalleau (2009). Justifying and Generalizing Contrastive Divergence, in: Neural Computation, 21:6(1601--1621).
Carreau, J. and Bengio, Y. (2009). A Hybrid Pareto Model for Asymmetric Fat-Tailed Data: the univariate case, In Extremes
Nicolas Le Roux and Yoshua Bengio(2008). Representational Power of Restricted Boltzmann Machines and Deep Belief Networks, in: Neural Computation, volume 20, number 6, pages 1631-1649.
Bengio, Y., Senecal J.-S., (2008). Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model. IEEE Transactions on Neural Networks.
Yoshua Bengio and Olivier Delalleau (2009). Justifying and Generalizing Contrastive Divergence. Neural Computation, vol 21, number 1, pages 1-21.
Yoshua Bengio (2009), Learning Deep Architecture for AI, Foundations and Trends in Machine Learning, vol 2, number 1.
Hugo Larochelle, Yoshua Bengio, Jerome Louradour and Pascal Lamblin (2009), Exploring Strategies for Training Deep Neural Networks, in: Journal of Machine Learning Research, pages 1--40.
Kehoe, E. J., Olsen, K. N., Ludvig, E. A., Sutton, R. S. (2008), Scalar timing varies with response magnitude in classical conditioning of the rabbit nictitating membrane response (Oryctolagus cuniculus), Behavioral Neuroscience (in press, 21 manuscript pages).
Bhatnagar, S., Sutton, R. S., Ghavamzadeh, M., Lee, M.(accepted October 2007), Natural actor–critic algorithms, to appear in Automatica, 35 manuscript pages.
Ludvig, E. A., Sutton, R. S., Kehoe, E. J. (2008), Stimulus representation and the timing of reward-prediction errors in models of the dopamine system, Neural Computation 20:3034–3054.
Kehoe, E. J., Ludvig, E. A., Dudeney, J. E., Neufeld, J., Sutton, R. S. (2008), Magnitude and timing of nictitating membrane movements during classical conditioning of the rabbit (Oryctolagus cuniculus), Behavioral Neuroscience 122(2):471–476.
Shai Ben-David and R. Schuller (2008), A Notion of Task Relatedness Yielding Provable Multiple-Task Learning Guarantees, in Machine Learning Journal, to appear (18 pages).
Zhu, M. (2008), Kernels and Ensembles: Perspectives on Statistical Learning, in The American Statistician, 62, 97-109
Laflamme-Sanders, A. and Zhu, M. (2008) LAGO on the unit sphere, in Neural Networks (in press) doi:10.1016/j.neunet.2008.08.002
Zhu M., Zhang Z., Hirdes J. P., and Stolee P. (2007). Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Medical Informatics and Decision-Making, *7*:41.
James Bergstra, Normand Casagrande,Dumitru Erhan, Douglas Eck and Balazs Kegl(2006). Aggregate Features and AdaBoost for Music Classification. In: Journal Machine Learning.
Chapados, N. and Bengio, Y. (2007). Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization In: Journal of Computers, volume 2, number 1, pages 12-19.
Le Roux, N. and Bengio, Y. (2008). Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. in: Neural Computation, pp. 1631-1649.
Sonnenburg, S. et al. (inc. Vincent, P.) (2007). The Need for Open Source Software in Machine Learning. Journal of Machine Learning Research, accepted for publication on 2007/09/05.
Zhu, M. (2007). Kernels and ensembles: Perspectives on statistical learning. The American Statistician, *62*(2), 97 – 109.
Bengio, Y. (2007) On the Challenge of Learning Complex Functions. Computational Neuroscience: Theoretical Insights into Brain Function, Elsevier, 2007.
Bingham, D. R. and Chipman, H. A. (2007). Incorporating Prior Information in Optimal Design for Model Selection. Technometrics 49, 155-163
>Wang, X., Chipman, H. A., Salloum, G. A., Welch, W. J. and Young, S.S. (2006). Exploration of Cluster Structure-Activity Relationship Analysis in Efficient High-Throughput Screening. Journal of Chemical Information and Modeling, 47, 1206-1214
Zhu, M., Chen, W., Hirdes, J. P. and Stolee, P. (2007). The K-nearest neighbors algorithm predicted rehabilitation potential better than current clinical assessment protocol. Journal of Clinical Epidemiology, 60, 1015-1021.
Bengio, Y., Monperrus, M. and Larochelle, H. (2006). Non-Local Estimation of Manifold Structure. In Neural Computation, pp.2509-2528, vol 18.
Zhu, M. (2006). Discriminant analysis with common principal components. Biometrika, 93(4), 1018-1024.
Erhan, D., L'Heureux, P. J., Yue, S. Y. and Bengio, Y. (2006). Collaborative Filtering on a Family of Biological Targets. J. Chem. Inf. Model., 46(2):626-635, 2006.
Zhu, M. and Chipman, H. (2006). Darwinian evolution in parallel universes: A parallel genetic algorithm for variable selection. Technometrics, 48(4), 491 - 502.
Chipman, H. and Tibshirani, R. (2006). Hybrid Hierarchical Clustering With Applications To Microarray Data. Biostatistics 7, pp. 286-301.
Zhu, M. and Ghodsi, A. (2006). Automatic dimensionality selection from the screen plot via the use of profile likelihood. Comput. Stat. Data Anal., 51(2), pp. 918-930.
Poyton, A. A., Varziri, M. S., McAuley, K. B., McLellan, P. J. and Ramsay, J. O. (2006). Parameter estimation in continuous-time dynamic models using principal differential analysis. Computational Chemical Engineering 30, pp. 698-708.
Kustra, R., Shioda, R. and Zhu, M. (2006). A factor analysis model for functional genomics. BMC Bioinformatics 7:216.
Caetano, T., Caelli, T., Schuurmans, D. and Barrone, D. (2006). Graphical models and point pattern matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (10):1646-1663.
Boutilier, C., Patrascu, R., Poupart, P. and Schuurmans, D. (2006). Constraint-based optimization and utility elicitation using the minimax decision criterion. Artificial Intelligence 170 (8-9): 686-713.
Zhu, M., Su, W. and Chipman, H. A. (2006). LAGO: A computationally efficient approach for statistical detection. Technometrics, 48, 193-205.
Zhu, M., Hastie, T. J. and Walther, G. (2005). Constrained ordination analysis with flexible response functions. Ecological Modelling 187(4), pp. 524-536.
Biedl, T. and Wilkinson, D. (2005). Bounded-degree independent sets in planar graphs. Theory of Computing Systems, 38(3):253-278.
Stone, P., Sutton, R.S. and Kuhlmann, G. (2005). Reinforcement Learning for RoboCup-Soccer Keepaway. Adaptive Behavior, 13(3):165-188.
Chipman, H. A. and Gu, H. (2005). Interpretable Dimension Reduction. Journal of Applied Statistics.
Wang, S., Schuurmans, D., Peng, F., and Zhao, Y. (2005). Combining Statistical Language Models via the Latent Maximum Entropy Principle. Machine Learning 59(1): 1-22.
Hutchinson, R.A., McLellan, P.J., Ramsay, J.O., Sulieman, H. and Bacon, D.W. (2004). Investigating the impact of operating parameters on molecular weight distributions using functional regression. In Macromolecular Symposia, Wiley, Weinheim, Germany, 495-508.
Lam, R.L.H. and Welch, W. J. (2004). Comparison of Methods Based on Diversity and Similarity for Molecule Selection and the Analysis of Drug Discovery Data. In Chemoinformatics Concepts, Methods, and Tools for Drug Discovery, J. Bajorath, ed., Humana Press, NJ, pp. 301-315.
Huang, X., Peng, F., An, A., and Schuurmans, D. (2004). Dynamic web log session identification with statistical language models. Journal of the American Society for Information Science and Technology, 55, 14, 1290-1303.
L'Heureux, P.-J., Carreau, J., Bengio, Y., Delalleau, O. and Yue, S. Y. (2004). Locally Linear Embedding for dimensionality reduction in QSAR In Journal of Computer-Aided Molecular Design, 18:475-482.
Hutchinson, R.A., McLellan, P.J., Ramsay, J. O., Sulieman, H., and Bacon, D.W. (2004). Investigating the impact of operating parameters on molecular weight distributions using functional regression. Macromolecular Symposia, Wiley, Weinheim, Germany, 495-508.
Malfait, N., and Ramsay, J. O. (2004). The historical functional linear model. Canadian Journal of Statistics, 31, 115-128.
Bengio, Y., Delalleau, O., Le Roux, N., Paiement, J.-F., Vincent, P. and Ouimet, M. (2004). Learning Eigenfunctions Links Spectral Embedding and Kernel PCA. In Neural Computation, 16(10): 2197-2219.
Bengio, Y. and Grandvalet, Y. (2004). No Unbiased Estimator of the Variance of K-Fold Cross-Validation. In Journal of Machine Learning Research, 5: 1089-1105.
Peng, F., Schuurmans, D. and Wang, S. (2004). Augmenting Naive Bayes Classifiers with Statistical Language models. In Information Retrieval 7(3), 317-345.
Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2004). Learning Mixture Models with the Regularized Latent Maximum Entropy Principle. In IEEE Transactions on Neural Networks 15(4), 903-916.
Feng, J., Lurati L., Robinson T., Wang Y., Yuan S., Ouyang H., Young S., Banks T. (2003). Predictive Toxicology Benchmarking Molecular Descriptors and Statistical Methods. In Journal of Chemical Information and Computer Science, 43(5):1463-70.
Bengio, Y., and Chapados, N. (2003). Extensions of Metric-Based Model Selection. In Journal of Machine Learning Research (3) pp. 1209-1227.
Nadeau, C. and Bengio, Y. (2003). Inference for the Generalization Error. In Machine Learning Journal 52(3), pp. 239-281.
Ghosn J. and Bengio, Y. (2003). Bias Leaning, Knowledge Sharing. In IEEE Transactions on Neural Networks 14(4), pp. 748-765.
Bengio, Y., Ducharme, R., Vincent, P. and Jauvin, C. (2003). A Neural Probabilistic Language Model. In Journal of Machine Learning Research (3), pp. 1137-1155.
Collobert, R. Bengio, Y. and Bengio, S. (2003). Scaling Large Learning Problems with Hard Parallel Mixtures. In International Journal of Pattern Recognition and Artificial Intelligence 17(3), pp. 349-365.
Malfait, N. and Ramsay, J. O. (2003). The historical hunctional linear model. In Canadian Journal of Statistics.
Huang, X., Peng, F., Schuurmans, D., Cercon, N. and Robertson, S. (2003). Applying Machine Learning to Text Segmentation for Information Retrieval. In Information Retrieval 6(3), 333-362.
Lam, R. L. H., Welch, W. J., and Young, S. S. (2002). Uniform Coverage Designs for Molecule Selection. In Technometrics, 44, 99-109.
Young, S. S., Lam, R. L. H., and Welch, W. J. (2002). Initial Compound Selection for Sequential Screening. In Current Opinion in Drug Discovery and Development, 5(3), 422-427.
Schuurmans, D. and Southey, F. (2002). Metric-based methods for adaptive model selection and regularization. In Machine Learning, 48(1-3): 51-84. (Special issue on New Methods for Model Selection and Model Combination)
Samson, S., Zatorre, R. J. and Ramsay, J. O. (2002). Deficits of musical timbre perception after unilateral temporal-lobe lesion revealed with multidimensional scaling. In Brain, 125, 511-523.
Rossi, N., Wang, X. and Ramsay, J. O. (2002). Nonparametric item response function estimates with the EM algorithm. In Journal of the Behavioral and Educational Sciences, 27, 291-317.
Trentin E., Brugnara F., Bengio Y., Furlanello C. and De Mori R. (2002). Statistical and Neural Network Models for Speech Recognition. In Connectionist Approaches to Clinical Problems in Speech and Language, ed. R. Daniloff, Lawrence Erlbaum publ., p.213-264.
Chipman, H. A., George, E.I. and McCulloch, R.E. (2002). Discussion of 'Spline Adaptation in Extended Linear Models', by Hansen and Kooperberg. In Statistical Science, 17, 20-21.
Takeuchi, I., Bengio, Y., and Kanamori, T. (2002). Robust Regression with Asymmetric Heavy-Tail Noise Distributions. In Neural Computation, 14(10), pp. 2469-2496.
Collobert R., Bengio S., and Bengio Y. (2002). Parallel Mixture of SVMs for Very Large Scale Problems. In Neural Computation, 14(5), pp. 1105-1114.
Chapelle, O., Vapnik, V., and Bengio, Y. (2002). Model Selection for Small Sample Regression. In Machine Learning Journal, 48(1), pp.9-23.
Vincent, P., and Bengio, Y. (2002). Kernel Matching Pursuit. In Machine Learning Journal, 48(1), pp.165-187.
Chipman, H. A., George, E. I, and McCulloch, R. E. (2002). Bayesian Treed Models. In Machine Learning, 48, 299-320.
Schuurmans, D. and Southey, F. (2001). Local search characteristics of incomplete SAT procedures. In Artificial Intelligence, 132(2): 121-150. (Invited for submission and review as award winning paper from AAAI-2000)
Grove, A., Littlestone, N. and Schuurmans, D. (2001). General convergence results for linear discrimant updates. In Machine Learning, 43(3):173-210.
Cook, R.J and Lawless, J.F.(2001). Some comments on efficiency gains from auxiliary information for right-censored data. In Journal of Statistical Planning and Infererence, 96, 191-202
Chipman, H. A. and Gu, H. (2001). Discussion of 'Flexible regression modeling with adaptive logistic basis functions', by P. M. Hooper. In Canadian Journal of Statistics, 29, 370-374.
Chipman, H. A., George, E., and McCulloch, R. (2000). Hierarchical Priors for Bayesian CART Shrinkage. In Statistics and Computing, 10, 17-24.
Grenier, M. and Léger, C. (2000). Bootstrapping Regression Models with BLUS Residuals. In Canadian Journal of Statistics, 28, 31-43.
Léger, C. (2000). Discussion of "The Estimating Function Bootstrap" de Hu, F. Et Kalbfleisch, J.D. In Canadian Journal of Statistics, 28, 487-489.
Schwenk, H. and Bengio Y. (2000). Boosting Neural Networks. In Neural Computation, 12(8), pp. 1869-1887.
Bengio, Y. (2000). Gradient-Based Optimization of Hyper-Parameters. In Neural Computation, 12(8), pp. 1889-1900.
Bengio, S. and Bengio, Y. (2000). Taking on the Curse of Dimensionality in Joint Distributions Using Neural Networks. In IEEE Transactions on Neural Networks (special issue on data mining and knowledge discovery), 11(3), pp. 550-557.
Chipman, H. A., George, E., and McCulloch, R. (1998). Bayesian CART Model Search (with discussion). In Journal of the American Statistical Association, 93, 935-960.
Submitted papers:
Wang, X. and Chipman, H. (2009) A new constrained mixture model with application in drug discovery, submitted.
Franey, M., Ranjan, P. and Chipman, H. (2009) Branch and bound algorithms for maximizing expected improvement functions, submitted to JSPI, October 16, 2009.
Podder, M., Welch, W. J., Zamar, R. H., and Tebbutt, S. J. (2006). Dynamic Variable Selection in SNP Genotype Autocalling from APEX Microarray Data. Submitted to BMC Bioinformatics and revised October 6, 2006.
Loeppky, L., Bingham, D. and Welch, W. J. (2006). Computer Model Calibration or Tuning in Practice. Submitted to Technometrics, May 2006.
Other refereed contributions
Papers in refereed conference proceedings:James Bergstra, Yoshua Bengio, Pascal Lamblin, Guillaume Desjardins and Jerome Louradour (2010), Image classification with complex cell neural networks, in: Computational and systems neuroscience (COSYNE), Salt Lake City.
Yu, Y., Yang, M., Xu, L., White, M. and Schuurmans, D. (2010). Relaxed clipping: A global training method for robust regression and classification. Advances in Neural Information Processing Systems (NIPS-2010).
George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, Shai Ben-David, Yubin Kim (2010). ProbClean: A probabilistic duplicate detection system. ICDE 2010: 1193-1196.
Li, W., Xu, L. and Schuurmans, D. (2010). Facility locations revisited: An efficient belief propagation approach, In Proceedings of the IEEE International Conference on Automation and Logistics (ICAL'10). Received Best Paper in Logistics Award.
Shi, Y., Guo, Y., Lin, G. and Schuurmans, D. (2010). Kernel-based gene regulatory network inference, In Ninth Annual International Conference on Computational Systems Bioinformatics (CSB'2010).
Bergsma, S. and Lin, D. and Schuurmans, D. (2010). Improved natural language learning via variance-regularization support vector machines, In Proceedings of the Fourtheenth Conference on Computational Natural Language Learning, (CONLL'10).
Shi, Y., Hasan, M., Cai, Z., Lin, G. and Schuurmans, D. (2010). Linear coherent bi-cluster discovery via beam detection and sample set clustering. In Fourth Annual International Conference on Combinatorial Optimization and Applications, (COCOA'2010).
H.R. Maei, Cs. Szepesvari, S. Bhatnagar, and R.S. Sutton (2010). Toward Off-Policy Learning Control with Function Approximation, Proc. 27th Int'l Conf. on Machine Learning (ICML'10).
H.R. Maei, Cs. Szepesvari, S. Bhatnagar, D. Precup, D. Silver and R.S. Sutton (2009). Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation, Proc. Advances in Neural Information Processing Systems 23 (NIPS'09)
R. Sutton, H. R. Maei, D. Precup, S. Bhatnagar, D. Silver, Cs. Szepesvári, and E. Wiewiora (2009). Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation, Proc.26th Int'l Conf. on Machine Learning, (ICML'09), pp. 993–1000.
H.R. Maei and R.S. Sutton (2010). GQ(λ): A General Gradient Algorithm For Temporal-Difference Prediction Learning with Eligibility Traces, Proc. 3rd Conf. on Artificial General Intelligence, (AGI'10).
Joseph Turian, Lev Ratinov and Yoshua Bengio (2010). Word representations: A simple and general method for semi-supervised learning, in: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics(ACL2010), Uppsala, Sweden, pages 384--394, Association for Computational Linguistics.
Dumitru Erhan, Aaron Courville, Yoshua Bengio and Pascal Vincent (2010). Why Does Unsupervised Pre-training Help Deep Learning? in: Proceedings of AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, pages 201-208.
Yoshua Bengio and Xavier Glorot (2010). Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, pages 249-256.
Guillaume Desjardins, Aaron Courville and Yoshua Bengio (2010). Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machine, in: Proceedings of AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, pages 145-152.
James Bergstra, Michael Mandel and Douglas Eck (2010). Scalable Genre and Tag Prediction with Spectral Covariance, in: ISMIR, Utrecht, The Netherlands.
James Bergstra and Yoshua Bengio (2010) Slow, Decorrelated Features for Pretraining Complex Cell-like Networks, in: Advances in Neural Information Processing Systems 22 (NIPS'09).
Aaron Courville, Douglas Eck and Yoshua Bengio (2009). An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism, in: Neural Information Processing Systems Conference NIPS'22, pages 405-413.
Joseph Turian, Lev Ratinov, Yoshua Bengio and Dan Roth (2010). A preliminary evaluation of word representations for named-entity recognition, in: NIPS Workshop on Grammar Induction, Representation of Language and Language Learning.
Giuseppe Attardi, Felice Dell'Orletta, Maria Simi and Joseph Turian (2009), Accurate Dependency Parsing with a Stacked Multilayer Perceptron, in: Proceeding of Evalita 2009, Springer.
Shafiei, M.M. and Chipman, H. (2009) Mixed Membership Stochastic Blockmodels for Transactional Data, NIPS workshop on Analyzing Networks and Learning With Graphs.
Quadrianto, N., Caetano, T., Lim, J. and Schuurmans, D. (2010) Convex relaxation of mixture regression with efficient algorithms. In Advances in Neural Information Processing Systems (NIPS'09).
Yu, Y., Li, Y., Szepesvari, C. and Schuurmans, D. (2010) A general projection property for distribution families. In Advances in Neural Information Processing Systems (NIPS'09).
Yoshua Bengio, Jerome Louradour, Ronan Collobert and Jason Weston (2009). Curriculum Learning, in: Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09), ACM.
Margareta Ackerman and Shai Ben-David (2009). Measures of Clustering Quality: A Working Set of Axioms for Clustering, Proceedings of Neural Information Processing Systems (NIPS 2008)
S. Ben-David, Tyler Lu, David Pal, and Miroslava Stakova (2009). Learning Low-Density Separators, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.
Margarita Ackerman and S. Ben-David (2009). Which Data Sets are Clusterable? - A Theoretical Study of Clusterability, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.
Reza Bosagh Zadeh, Shai Ben-David (2009). A Uniqueness Theorem for Clustering, Proceedings of UAI 2009.
Shai Ben-David, David Pal and Shai Shalev-Shwartz (2009). Agnostic Online Learning. Proceedings of COLT 2009.
George Beskales, Mohamed A. Soliman, Ihab F. Ilyas and Shai Ben-David (2009). Modeling and Querying Possible Repairs in Duplicate Detection. Proceedings of VLDB 2009.
Philippe Hamel, Sean Wood and Douglas Eck (2009). Automatic Identification of Instrument Classes in Polyphonic and Poly-Instrument Audio, in: 10th International Society for Music Information Retrieval Conference, Kobe, Japan, pages 399--404.
Xu, L., White, M. and Schuurmans, D. (2009) Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. In International Conference on Machine Learning (ICML-09). *Received Honorable Mention for Best Overall Paper, ICML-09 *All authors from my research group
Xu, L., Li, W. and Schuurmans, D. (2009) Fast normalized cut with linear constraints. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR-09). *All authors from my research group
Li, Y., Szepesvari, C. and Schuurmans, D. (2009) Learning exercise policies for American options. In International Conference on Artificial Intelligence and Statistics (AISTATS-09).
Yang, M., Li, Y. and Schuurmans, D. (2009) Dual temporal difference learning. In International Conference on Artificial Intelligence and Statistics (AISTATS-09). *All authors from my research group
Guo, Y. and Schuurmans, D. (2009) A reformulation of support vector machines for general confidence functions. In Asian Conference on Machine Learning (ACML-09).
Shi, Y., Cai, Z., Lin, G. and Schuurmans, D. (2009) Linear coherent bi-cluster discovery via line detection and sample majority voting. In International Conference on Combinatorial Optimization and Applications (COCOA-09).
Li, Y., Cheng, L. and Schuurmans, D. (2009) Inference of the structural credit risk model using MLE. In IEEE Symposium on Computational Intelligence for Financial Engineering (CIFEr-09).
M. Cutumisu, D. Szafron, M.Bowling, R.S. Sutton (2008) Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games, Proc. of the 4th Conf. on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 08), pp. 22-29.
E.A. Ludvig and A. Koop, (2008) Learning to Generalize Through Predictive Representations: A Computational Model of Mediated Conditioning, From Animal to Animats 10: Proc. of Simulation of Adaptive Behavior (SAB 08), pp. 342-351.
E.A. Ludvig, R.S. Sutton, E. Verbeek, and J. Kehoe (2009). A Computational Model of Hippocampal Function in Trace Conditioning, Proc. Conf. on Neural Information Processing Systems (NIPS 08), 2009, 24% acceptance
R. S. Sutton, Cs. Szepesvari, H. R. Maei (2009). A Convergent O(n) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation, Proc. Conf. on Neural Information Processing Systems (NIPS 08), 2009. 24% acceptance
D. Silver and G. Tesauro (2009). Monte-Carlo Simulation Balancing, Proc. of the 26th International Conf. on Machine Learning.
R. S. Sutton, H. Maei, D. Precup, S. Bhatnagar, D. Silver, C. Szepesvari and E. Wiewiora (2009) Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation, Proc. of the 26th International Conf. on Machine Learning.
Bouveyron, C., Chipman, H. and Come, E. (2009) Supervised Classification and Visualization of Social Networks Based on a Probabilistic Latent Space Model, 7th International Workshop on Mining and Learning with Graphs, Leuven, Belgium, refereed poster presentation.
Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio and Pascal Vincent (2009). The Difficulty of Training Deep Architectures and the effect of Unsupervised Pre-Training, , in: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009), Clearwater (Florida), USA.
François Maillet, Douglas Eck, Guillaume Desjardins and Paul Lamere (2009). Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists, , in: Proceedings of the 10th International Conference on Music Information Retrieval, Kobe, Japan.
Joseph Turian, James Bergstra and Yoshua Bengio (2009) Quadratic Features and Deep Architectures for Chunking, , in: North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT).
Hugo Larochelle, Dumitru Erhan and Pascal Vincent (2009). Deep Learning using Robust Interdependent Codes, in: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009), Clearwater (Florida), USA.
Yoshua Bengio, Jerome Louradour, Ronan Collobert and Jason Weston (2009). Curriculum, in: International Conference on Machine Learning proceedings.
Sutton, R. S., Szepesvari, Cs., Maei, H. R., (2009) A convergent O(n) algorithm for off-policy temporal-difference learning with linear function approximation, Advances in Neural Information Processing Systems 21 (8 pages). MIT Press.
Ludvig, E., Sutton, R. S., Verbeek, E., Kehoe, E. J., (2009) A computational model of hippocampal function in trace conditioning, Advances in Neural Information Processing Systems 21 (to appear, 8 pages). MIT Press.
Wang, Q. and Lin, D. and Schuurmans, D. (2008), Semi-supervised convex training for dependency parsing, Proceedings of the Forty-sixth Annual Conference of the Association for Computational Linguistics: Human Language Technologies (ACL).
Li, Y. and Schuurmans, D. (2008), Policy iteration for learning an exercise policy for American options, Proceedings of the European Workshop on Reinforcement Learning (EWRL).
Guo, Y. and Schuurmans, D. (2008), Convex relaxations of latent variable training, Advances in Neural Information Processing Systems (NIPS) 20.
Guo, Y. and Schuurmans, D. (2008), Discriminative batch mode active learning, Advances in Neural Information Processing Systems (NIPS) 20.
Wang, T. and Lizotte, D. and Bowling, M. and Schuurmans, D. (2008), Stable dual dynamic programming, Advances in Neural Information Processing Systems (NIPS) 20.
Margarita Ackerman, Shai Ben-David (2008), Measures of Clustering Quality: A Working Set of Axioms for Clustering. NIPS 2008.
Shai Ben-David, Tyler Lu, David Pal (2008), Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning, COLT 2008:33-4.
Shai Ben-David, Ulrike von Luxburg (2008), Relating Clustering Stability to Properties of Cluster Boundaries, COLT 2008:379-390
Cutumisu, M., Szafron, D., Bowling, M. and Sutton R. S., (2008) Agent learning using action-dependent learning rates in computer role-playing games, Proceedings of the 4th Conference on Artificial Intelligence and Interactive Digital Entertainment (to appear, 8 pages).
Sutton, R. S., Szepesvari, Cs. and Geramifard, A., Bowling, M., (2008) Dyna-style planning with linear function approximation and prioritized sweeping, Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence.
Silver, D., Sutton, R. S. and Müller, M., (2008) Sample-based learning and search with permanent and transient memories, Proceedings of the 25th International Conference on Machine Learning. (27% Acceptance)
Bhatnagar, S., Sutton, R. S., Ghavamzadeh, M. and Lee, M., (2008) Incremental natural actor-critic algorithms, Advances in Neural Information Processing Systems 20.
Sutton, R. S., Koop, A. and Silver, D., (2007) On the role of tracking in stationary environments, Proceedings of the 24th International Conference on Machine Learning.
Silver, D., Sutton, R. S. and Müller, M., (2007) Reinforcement learning of local shape in the game of Go, Proceedings of the 20th International Joint Conference on Artificial Intelligence.
Geramifard, A., Bowling, M., Zinkevich, M. and Sutton, R. S., iLSTD: Eligibility traces and convergence analysis, Advances in Neural Information Processing Systems 19, 2007.
Yoshua Bengio (2008), Neural net language models, in: Scholarpedia, volume 3, number 1, pages 3881.
Nicolas Chapados and Yoshua Bengio (2008), Augmented Functional Time Series Representation and Forecasting with Gaussian Processes, in: NIPS2007, 2008.
Balázs Kégl, Thierry Bertin-Mahieux and Douglas Eck (2008), Metropolis-Hastings Sampling in a FilterBoost Music Classifier, in: Music and machine learning workshop (ICML08), 2008.
Hugo Larochelle and Yoshua Bengio (2008), Classification using Discriminative Restricted Boltzmann Machines, in: International Conference on Machine Learning proceedings, 2008.
Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau and Balázs Kégl (2008), Learning the 2-D Topology of Images, in: NIPS2007, 2008.
Nicolas Le Roux, Pierre-Antoine Manzagol and Yoshua Bengio (2008), Topmoumoute online natural gradient algorithm, in: NIPS2007, 2008.
Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux and Stephen Green, Automatic Generation of Social Tags for Music Recommendation, in: NIPS2007, 2008.
Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle, Greedy Layer-Wise Training of Deep Networks, in: Advances in Neural Information Processing Systems 19, pages 153-160, MIT Press, 2007.
Pierre-Antoine Manzagol and Thierry Bertin-Mahieux, On the Use of Sparse Time-Relative Auditory Codes for Music, ISMIR2008, 2008. Best student paper.
Guo, Y. and Schuurmans, D. (2008), Efficient global optimization for exponential family PCA and low-rank matrix factorization, Proceedings of the Forty-sixth Annual Allerton Conference on Communication, Control, and Computing (Allerton).
Yoshua Bengio(2008) Neural net language models. Journal Machine Learning.
Neural net language models, Yoshua Bengio, in: Scholarpedia, volume 3, number 1, pages 3881, 2008.
James Bergstra, Alexandre Lacoste and Douglas Eck(2006) Predicting Genre Labels for Artists using FreeDB. Proc. 7th International Conference on Music Information Retrieval (ISMIR).
Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio(2008) Zero-data Learning of New Tasks. In AAAI Conference on Artificial Intelligence (AAAI2008).
Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol(2008) Extracting and Composing Robust Features with Denoising Autoencoders. In International Conference on Machine Learning proceedings (ICML2008).
Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol(2008) Classification using Discriminative Restricted Boltzmann Machines. In International Conference on Machine Learning proceedings (ICML2008).
Nicolas Chapados and Yoshua Bengio (2007), Forecasting Commodity Contract Spreads with Gaussian Process, in: 13th Intarnational Conference on Computing in Economics and Finance, June 2007.
Wang, T., Lizotte, D., Bowling, M. and Schuurmans, D. (2007) Stable dual dynamic programming, in Advances in Neural Information Processing Systems (NIPS*2007).
Bouveyron, C. and Chipman, H. (2007) Visualization and classification of graph-structured data: the case of the Enron dataset, 20th International Joint Conference on Neural Networks
Chipman, H. A., George, E. I. and McCulloch, R. E. (2007). Bayesian Ensemble Learning. Advances in Neural Information Processing Systems 19, B. Scholkopf and J. Platt and T. Hoffman, Eds., 265–272, MIT Press, Cambridge, MA. Note: This was among the 25 papers selected from 831 submissions for full oral presentation at the NIPS meeting in Vancouver.
A. Geramifard, M. Bowling, M. Zinkevich, R. Sutton, "iLSTD: Eligibility Traces and Convergence Analysis", in Advances in Neural Information Processing Systems 20 (NIPS-06), 2007. (24% acceptance)
S. Gelly, and D. Silver, Combining Online and Offline Knowledge in UCT, Proc. of the 24th Int’l Conf. on Machine Learning, (ICML-07), 2007. 29% acceptance rate.
M. Ghavamzadeh, and Y. Engel, Bayesian Policy Gradient Algorithms, Proc. Neural Information Processing Systems 20 (NIPS-06), 2007. 24% acceptance rate.
M. Ghavamzadeh and Y. Engel, Bayesian Actor-Critic Algorithms, Proc. of the 24th Int’l Conf. on Machine Learning (ICML-07), 2007. 29% acceptance rate.
R.S. Sutton, A. Koop, and D. Silver, On the Role of Tracking in Stationary Environ-ments, Proc. of the 24th Int'l Conf. on Machine Learning, (ICML-07), 2007. 29% acceptance rate.
D. Silver, R.S. Sutton, and M. Mueller, Reinforcement Learning of Local Shape in the Game of Go, Proc. of the 20th Int'l Joint Conf. on Artificial Intelligence, (IJCAI-07), 2007. 35% acceptance rate.
B. Tanner, V. Bulitko, A. Koop, and C. Paduraru, Grounding Abstraction in Predictive State Representations, Proc. of the 20th Int’l Joint Conf. of Artificial Intelligence (IJCAI-07), 2007. 35% acceptance rate.
S. Bahatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee, “Incremental Natural Actor-critic Algorithms,” Proc. Neural Information Processing Systems 20 (NIPS-07), pages 105-112, 2008. (22% acceptance) http://www.cs.ualberta.ca/~sutton/papers/BSGL-08.pdf
Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau and Balázs Kégl (2008). Learning the 2-D Topology of Images In: NIPS2007.
Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle (2007). Greedy Layer-Wise Training of Deep Networks. In: Advances in Neural Information Processing Systems 19, pages 153-160, MIT Press.
Nicolas Chapados and Yoshua Bengio (2008). Augmented Functional Time Series Representation and Forecasting with Gaussian Processes. In: NIPS2007.
Nicolas Le Roux and Yoshua Bengio 2007. Continuous Neural Networks. In: AISTAT2007.
Nicolas Le Roux, Pierre-Antoine Manzagol and Yoshua Bengio (2008). Topmoumoute online natural gradient algorithm. In: NIPS2007.
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra and Yoshua Bengio (2007). An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation. In: ICML2007, pages 473-480.
Carreau, J., and Bengio, Y. (2007). A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data. Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, Puerto Rico.
Yoshua Bengio and Yann Le Cun (2007). Scaling Learning Algorithms towards AI. Book chapter in: Large Scale Kernel Machines, MIT Press, 2007.
Wang, Q., Lin, D. and Schuurmans, D. (2007). Simple training of dependency parsers via structured boosting. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Lizotte, D., Wang, T., Bowling, M. and Schuurmans, D. (2007). Automatic gait optimization with Gaussian process regression. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Bengio, Y., Lamblin, P., Popovici, D. and Larochelle, H. (2007). Greedy Layer-Wise Training of Deep Networks. Advances in Neural Information Processing Systems 19.
Cheng, L., Vishwanathan, S., Schuurmans, D., Wang, S. and Caelli, T. (2007). Implicit online learning with kernels. Advances in Neural Information Processing Systems 19.
Lee, C., Wang, S., Jiao, F., Schuurmans, D. and Greiner, R. (2007). Learning to model spatial dependency: Semi-supervised discriminative random fields. Advances in Neural Information Processing Systems 19.
Zhou, D., Huang, J. and Schoelkopf, B. (2007). Learning with hypergraphs: clustering, classification, and embedding. >Advances in Neural Information Processing Systems 19.
Huang, J., Smola, A., Gretton, A., Borgwardt, K. and Schoelkopf, B. (2007). Correcting sample selection bias by unlabeled data. Advances in Neural Information Processing Systems 19.
Ghodsi, A., Southey, F. and Wilkinson, D. (2007). Improving embeddings by flexible exploitation of side information. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Southey, F., Loh, W. and Wilkinson, D. (2007). Inferring complex agent motions from partial trajectory observations. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Guo, Y. and Greiner, R. (2007). Optimistic active learning using mutual information. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Geramifard, A., Bowling, M., Sutton, R. S. (2006). Incremental Least-Squares Temporal Difference Learning. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 356-361.
Zhu, M., Chen, W., Hirdes, J. and Stolee, P. (2006). Predicting rehabilitation potential with the K-nearest neighbors algorithm: A comparison with the current clinical assessment protocol. In Proceedings of the 17th IASTED International Conference on Modelling and Simulation, 110 - 115.
Bowling, M, McCracken, P., James, M., Neufeld, J., and Wilkinson, D. (2006). Learning predictive state representations using non-blind policies. In Proceedings of the Twenty-third International Conference on Machine Learning.
Huang, J., Zhu, T., Greiner, R., Zhou, D. and Schuurmans, D. (2006). Information marginalization on subgraphs. In Proceedings of the Seventeenth European Conference on Machine Learning and the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases.
Huang, J., Zhu, T. and Schuurmans, D. (2006). Web community identification from random walks. In Proceedings of the Seventeenth European Conference on Machine Learning and the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases.
Guo, Y. and Schuurmans, D. (2006) Convex structure learning for Bayesian networks: Polynomial feature selection and approximate ordering. In Proceedings of the Twenty-second Conference on Uncertainty in Artificial Intelligence.
Xu, L., Wilkinson, D., Southey, F. and Schuurmans, D. (2006). Discriminative unsupervised learning of structured predictors. In Proceedings of the Twenty-third International Conference on Machine Learning.
Bowling, M., Wilkinson, D. and Ghodsi, A. (2006). Subjective mapping. In Proceedings New Scientific and Technical Advances in Research (NECTAR) at AAAI 2006.
Xu, L., Crammer, K. and Schuurmans, D. (2006). Robust support vector machine training via convex outlier ablation. In Proceedings of the Twenty-first National Conference on Artificial Intelligence.
Milstein, A. and Wang, T. (2006). Localization with dynamic motion models: determining motion model parameters dynamically in Monte Carlo localization. In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics.
Wang, T., Poupart, P., Bowling, M. and Schuurmans, D. (2006). Compact, convex upper bound iteration for approximate POMDP planning. In Proceedings of the Twenty-first National Conference on Artificial Intelligence.
Jiao, F., Wang, S., Lee, C., Greiner, R. and Schuurmans, D. (2006). Semi-supervised conditional random fields for improved sequence segmentation and labeling. In Proceedings of the Joint Conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics.
Cheng, L., Wang, S., Schuurmans, D., Caelli, T. and Vishwanathan, S. (2006). An online discriminative approach to background subtraction. In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance.
Wang, Q., Cherry, C., Lizotte, D. and Schuurmans, D. (2006). Improved large margin dependency parsing via local constraints and Laplacian regularization. In Proceedings of the Tenth Conference on Computational Natural Language Learning.
Jiao, F., Xu, J., Yu, L. and Schuurmans, D. (2006). Protein fold recognition using the gradient boost algorithm. In Proceedings of the Fifth Computational Systems Bioinformatics Conference.
Precup, D., Sutton, R.S., Paduraru, C. (2006). Off-policy Learning with Recognizers. In Advances in Neural Information Processing Systems 18, pp. 1097-1104.
Sutton, R.S., Rafols, E. and Koop, A. (2006). Temporal abstraction in temporal-difference networks. In Advances in Neural Information Processing Systems 18, pp. 1313-1320.
Bengio, Y., Larochelle, H. and Vincent, P. (2006). Non-Local Manifold Parzen Windows. In Advances in Neural Information Processing Systems 18, pp. 115-122.
Bengio, Y., Delalleau, O. and Le Roux, N. (2006). The Curse of Highly Variable Functions for Local Kernel Machines. In Advances in Neural Information Processing Systems 18, pp. 107-114.
Bengio, Y., Le Roux, N., Vincent, P., Delalleau, O. and Marcotte, P. (2006). Convex Neural Networks. In Advances in Neural Information Processing Systems 18, pp. 123-130.
Wilkinson, D., Bowling, M. and Ghodsi, A. (2005). Learning subjective representations for planning. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Bowling, M., Ghodsi, A. and Wilkinson, D. (2005). Action respecting embedding. In Proceedings of the Twenty-second International Conference on Machine Learning.
Wang, Q., Schuurmans, D. and Lin, D. (2005). Strictly lexical dependency parsing. In Proceedings of 9th International Workshop on Parsing Technologies.
Bowling, M., Wilkinson, D., Ghodsi, A. and Milstein, A. (2005). Subjective localization with action respecting embedding. In Proceedings of the International Symposium of Robotics Research.
Milstein, A. (2005). Dynamic maps in Monte Carlo localization. In Proceedings of the Eighteenth Conference of the Canadian Society for Computational Studies of Intelligence.
Holte, R., Southey, F., Xiao, G. and Wilkinson, D. (2005). Software testing by active learning for commercial games. In Proceedings of the Twentieth National Conference on Artificial Intelligence.
Wang, Q. and Schuurmans, D. (2005). Improved estimation for unsupervised part-of-speech tagging. In Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering.
Guo, Y. and Greiner, R. (2005). Discriminative model selection for belief net structures. In Proceedings of the Twentieth National Conference on Artificial Intelligence.
Tanner, B. and Sutton, R.S. (2005). Temporal-Difference Networks with Eligibility Traces. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, August 7 - 11, 2005.
Tanner, B. and Sutton, R.S. (2005). Temporal-Difference Networks with History. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, July 30th - August 5th, 2005.
Rafols, E.J., Ring, M.B., Sutton, R.S. and Tanner, B. (2005). Using Predictive Representations to Improve Generalization in Reinforcement Learning. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, July 30th - August 5th, 2005.
Silver, D. (2005). Cooperative Pathfinding. In Proceedings of the 1st Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2005.
Sutton, R.S. and Tanner, B. (2005). Temporal-difference Networks. In Advances in Neural Information Processing Systems 17, pages 1377-1384.
Ghodsi, A., Southey, F., Huang, J. and Schuurmans, D. (2005). Tangent corrected embedding. In IEEE International Conference on Computer Vision and Pattern Recognition.
Boutilier, C., Patrascu, R., Poupart, P. and Schuurmans, D. (2005). Regret-based utility elicitation in contraint-based decision problems. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Milstein, A. (2005). Dynamic maps in Monte Carlo localization. In Proceedings of the Eighteenth Conference of the Canadian Society for Computational Studies of Intelligence (Canadian AI 2005).
Guo, Y., Greiner, R. and Schuurmans, D. (2005). Learning coordination classifiers. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
Cheng, L., Jiao, F., Schuurmans, D. and Wang, S. (2005). Variational Bayesian image modelling. In Proceedings of the Twenty-second International Conference on Machine Learning.
Wang, S., Wang, S., Greiner, R., Schuurmans, D. and Cheng, L. (2005). Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. In Proceedings of the Twenty-second International Conference on Machine Learning.
Zhou, D., Huang, J. and Schoelkopf, B. (2005). Learning from Labeled and Unlabeled Data on a Directed Graph. In Proceedings of the Twenty-second International Conference on Machine Learning.
Wang, T., Lizotte, D., Bowling, M. and Schuurmans, D. (2005). Bayesian sparse sampling for on-line reward optimization. In Proceedings of the Twenty-second International Conference on Machine Learning.
Xu, L. and Schuurmans, D. (2005). Unsupervised and semi-supervised multi-class support vector machines. In Proceedings of the Twentieth National Conference on Artificial Intelligence.
Guo, Y., Wilkinson, D. and Schuurmans, D. (2005). Maximum margin Bayesian networks. In Conference on Uncertainty in Artificial Intelligence.
Ouimet, M. and Bengio, Y. (2005). Greedy Spectral Embedding. In Cowell, R. G. and Ghahramani, Z., editors, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 253-260. Society for Artificial Intelligence and Statistics.
Morin, F. and Bengio, Y. (2005). Hierarchical Probabilistic Neural Network Language Model. In Cowell, R. G. and Ghahramani, Z., editors, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 246-252. Society for Artificial Intelligence and Statistics.
Delalleau, O., Bengio, Y. and Le Roux, N. (2005). Efficient Non-Parametric Function Induction in Semi-Supervised Learning. In Cowell, R. G. and Ghahramani, Z., editors, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 96-103. Society for Artificial Intelligence and Statistics.
Boufaden, N., Bengio, Y. and Lapalme, G. P. (2004). Approche statistique pour le repérage de mots informatifs dans les textes oraux. In Traitement Automatique du Langage Naturel 2004.
Xu, L., Neufeld, J., Larson, B. and Schuurmans, D. (2004). Maximum Margin Clustering.. In Saul, L.K., Weiss, Y. and Bottou, L., editors, Advances in Neural Information Processing Systems 17, p.1537-1544.
Ghodsi, A., Huang, J. and Schuurmans, D. (2004). Transformation-Invariant Embedding for Image Analysis. In Processing of the Eighth European Conference on Computer Vision (ECCV-04).
Rivest, F., Bengio, Y., and Kalaska, J. (2004). Brain Inspired Reinforcement Learning. In Saul, L.K., Weiss, Y. and Bottou, L., editors, Advances in Neural Information Processing Systems 17, p.1129-1136.
Bengio, Y., and Monperrus, M. (2004). Non-Local Manifold Tangent Learning. In Saul, L.K., Weiss, Y. and Bottou, L., editors, Advances in Neural Information Processing Systems 17, p.129-136.
Grandvalet, Y. and Bengio, Y. (2004). Semi-supervised Learning by Entropy Minimization. In Saul, L.K., Weiss, Y. and Bottou, L., editors, Advances in Neural Information Processing Systems 17, p.529-536.
Bhattacharya, I., Getoor, L. and Bengio, Y. (2004). Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models. In Proceedings of the conference of the Association for Computational Linguistics (ACL'2004).
Bengio, Y., Paiement, J.-F., Vincent, P., Delalleau, O., Le Roux, N. and Ouimet, M. (2004). Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. In Advances in Neural Information Processing Systems 16.
Bengio, Y. and Grandvalet, Y. (2004). No Unbiased Estimator of the Variance of K-Fold Cross-Validation. In Advances in Neural Information Processing Systems 16.
Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2003). Semantic N-gram language modeling with the latent maximum entropy principle. In the 28th IEEE International Conference on Acoustics, Speech, and Signal Processing.
Wang, S., Schuurmans, D. and Peng, F. (2003). Latent maximum entropy approach to semantic n-gram language modeling. In Ninth International Workshop on Artificial Intelligence and Statistics.
Chipman, H. A., George, E.I. and McCulloch, R.E. (2003). Bayesian Treed Generalized Linear Models. In Bayesian Statistics 7.
Wang, S., Schuurmans, D., Peng, F. and Zhao, Y.(2003) Boltzmann machine learning with the latent maximum entropy principle. In Uncertainty in Artificial Intelligence (UAI'03).
Lu, F. and Schuurmans, D. (2003). Monte Carlo matrix inversion policy evaluation. In Uncertainty in Artificial Intelligence (UAI'03).
Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2003). Learning mixture models with the latent maximum entropy principle. In International Conference on Machine Learning (ICML'03).
Boutilier, C., Patrascu, R., Poupart, P. and Schuurmans, D. (2003). Constraint-based optimization and elicitation with the minimax decision criterion. Ninth International Conference on Principles and Practice of Constraint Programming.
Chipman, H. A., Hastie, T., and Tibshirani, R. (2003). Clustering Microarray Data. In Statistical Analysis of Gene Expression Microarray Data, T. Speed, Ed., CRC Press.
Peng, F., Schuurmans, D. and Wang, S. (2003). Language and task independent text categorization with simple language models. In North American Association for Computational Linguistics (HLT-NAACL-03).
Lu, F. and Schuurmans, D. (2003). Model-based least-squares policy evaluation. In Canadian AI Conference (CAI-03).
Huang, X., Peng, F., An, A., Schuurmans, D. and Cercone, N. (2003). Session boundary detection for association rule learning. In Canadian AI Conference (CAI-03).
Peng, F., Schuurmans, D. and Wang, S. (2003). Language independent author attribution using character level language models. In European Association for Computational Linguistics (EACL-03).
Peng, F. and Schuurmans, D. (2003). Combining naive Bayes and n-gram language models for text classification. In European Conference on Information Retrieval Research (ECIR-03).
Wang, S. and Schuurmans, D. (2003). Learning latent variable models with Bregman divergences. In IEEE International Symposium on Information Theory (ISIT-03).
Ghodsi, A. and Schuurmans, D. (2003). Automatic basis selection for RBF networks using Stein's unbiased risk estimator. In International Joint Conference on Neural Networks (IJCNN-03).
Bengio Y. and Senecal, J.-S. (2003). Quick Training of Probabilistic Neural Nets by Importance Sampling. In Proceedings of the conference on Artificial Intelligence and Statistics (AISTATS'2003).
Vincent, P. and Bengio, Y. (2003). Manifold Parzen Windows. In Advances in Neural Information Processing Systems 15. MIT Press, Cambridge, MA.
Southey, F., Schuurmans, D. and Ghodsi A. (2002). Regularized greedy importance sampling. In Advances in Neural Information Processing Systems 15.
Collobert, R., Bengio Y., and Bengio S. (2002). Scaling Large Learning Problems with Hard Parallel Mixtures. In Pattern Recognition with Support Vector Machines, Springer, pp. 8-23.
Bengio, Y., I. Takeuchi, and T. Kanamori (2002). The Challenge of Non-Linear Regression on Large Datasets with Asymmetric Heavy Tails. In Proceedings of the Joint Statistical Meeting, CD-ROM, American Statistical Association publ.
Bengio, Y. and N. Chapados (2002). Metric-based Model Selection for Time-Series Forecasting. In H. Bourlard et. al (eds.) Neural Networks for Signal Processing XII. IEEE Press, 2002, pp. 13-24.
Vincent, P. and Bengio, Y. (2002). K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms. In T. G. Dietterich, S. Becker, and Z. Ghahramani (eds.) Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA, 2002, pp. 985-992.
Collobert, R., Bengio, S., and Bengio, Y. (2002). A Parallel Mixture of SVMs for Very Large Scale Problems. In T. G. Dietterich, S. Becker, and Z. Ghahramani (eds.) Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA, 2002, pp. 633-640.
Chapados N., Bengio Y., Vincent P., Ghosn J., Dugas C., Takeuchi I.,and Meng L. (2002). Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference. In T. G. Dietterich, S. Becker, and Z. Ghahramani (eds.) Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA, 2002, pp. 1369-1376.
Peng, F., Huang, X., Schuurmans, D. and Cercone, N. (2002). Investigating the relationship between word segmentation performance and retrieval performance in Chinese IR. In Proceedings of the Nineteenth International Conference on Computational Linguistics (COLING-02).
Peng, F., Huang, X., Schuurmans, D., Cercone, N. and Roberston, S. (2002). Using self-supervised word segmentation in Chinese information retrieval. In Proceedings of Twenty-fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-02).
Lu, F., Patrascu, R. and Schuurmans, D. (2002). Investigating the maximum likelihood alternative to TD(lambda). In Proceedings of the Nineteenth International Conference on Machine Learning (ICML-02).
Guestrin, C., Patrascu, R. and Schuurmans, D. (2002). Algorithm-directed exploration for model-based reinforcement learning in factored MDPs. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML-02).
Elidan, G., Ninio, M. Friedman, N. and Schuurmans, D. (2002). Data perturbation for escaping local maxima in learning. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02).
Patrascu, R., Poupart, P., Schuurmans, D. Boutilier, C. and Guestrin, C. (2002). Greedy linear value-approximation for factored Markov decision processes. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02).
Poupart, P., Boutilier, C., Patrascu, R. and Schuurmans, D. (2002). Piecewise linear value function approximation for factored MDPs. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02).
Wang, S., Rosenfeld, R., Zhao, Y. and Schuurmans, D. (2002). The latent maximum entropy principle. In Proceedings of the IEEE International Symposium on Information Theory (ISIT-02).
Schuurmans, D. and Patrascu, R. (2001). Direct value-approximation for factored MDPs. In Advances in Neural Information Processing Systems (NIPS*2001).
Schuurmans, D., Southey, F. and Holte, R. (2001). The exponentiated subgradient algorithm for heuristic Boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01).
Peng, F. and Schuurmans, D. (2001). A simple closed-class/open-class factorization for language modeling. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium (NLPRS-2001).
Peng, F. and Schuurmans, D. (2001). A hierarchical EM approach to word segmentation. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium (NLPRS-2001).
Peng, F. and Schuurmans, D. (2001). Self-supervised Chinese word segmentation. In Proceedings of the Fourth International Conference on Intelligent Data Analysis (IDA-01).
Chipman, H. A., George, E. I. and McCulloch, R. E. (2001). Managing Multiple Models. In Artificial Intelligence and Statistics 2001, Tommi Jaakkola, Thomas Richardson, Eds. , 11-18.
Boufaden, N., Lapalme, G. and Bengio Y. (2001). Topic segmentation : First Stage of Dialogue-Based Information extraction Process. In Proceedings of the Natural Language Pacific Rim Symposium, NLPRS-01.
Bengio, Y., Ducharme, R. and Vincent, P. (2001). A Neural Probabilistic Language Model. In Advances in Neural Information Processing Systems 13, pp.933-938.
Dugas, C., Bengio Y., Belisle F., Nadeau C. and Garcia R. (2001). A Universal Approximator of Convex Functions Applied to Option Pricing. In Advances in Neural Information Processing Systems 13, pp. 472-478.
Bengio, Y. and Bengio, S. (2000). Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks. In Advances in Neural Information Processing Systems 12, pp.400-406.
Monographs, books and book chapters:
Shai Ben-David (2008), Data Representation Framework Addressing the Training/Test Distributions Gap, (14 pages), In Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer and Neil D. Lawrence, editors, Dataset Shift in machine learning, MIT press.
Bengio, Y. and Le Cun, Y. (2007). Scaling learning algorithms towards AI. In Bottou, L., Chapelle, O., DeCoste, D. and Weston, J., editors, Large Scale Kernel Machines, MIT Press.
Schuurmans, D., Southey, F., Wilkinson, D. and Guo, Y. (2006). Metric-based approaches for semi-supervised regression and classification. In Chapelle, O., Schölkopf, B. and Zien, A., editors, Semi-Supervised Learning, pp. 421-451, MIT Press.
Delalleau, O., Bengio, Y. and Le Roux, N. (2006). Large-Scale Algorithms. In Chapelle, O., Schölkopf, B. and Zien, A., editors, Semi-Supervised Learning, pp. 333-341, MIT Press.
Bengio, Y., Delalleau, O. and Le Roux, N. (2006). Label Propagation and Quadratic Criterion. In Chapelle, O., Schölkopf, B. and Zien, A., editors, Semi-Supervised Learning, pp. 193-216, MIT Press.
Grandvalet, Y. and Bengio, Y. (2006). Entropy Regularization. In Chapelle, O., Schölkopf, B. and Zien, A., editors, Semi-Supervised Learning, pp. 151-168, MIT Press.
Bengio, Y., Delalleau, O., Le Roux, N., Paiement, J.-F., Vincent, P. and Ouimet, M. (2006). Spectral Dimensionality Reduction. In Guyon, I., Gunn, S., Nikravesh, M. and Zadeh, L., editors, Feature Extraction, Foundations and Applications, Springer.
Chipman, H. A. (2006). Prior Distributions for Bayesian Analysis of Screening Experiments. Chapter in Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, A. Dean and S.M.Lewis, Editors, 235-267. New York: Springer-Verlag.
Schonlau, M. and Welch, W. J. (2006). Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization. Chapter in Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, A. Dean and S.M.Lewis, Editors, 308-327. New York: Springer-Verlag.
Croteau, P., Cléroux, R. and Léger, C. (2005). Bootstrap Confidence Intervals for Periodic Replacement Policies. In Statistical Modeling and Analysis for Complex Data Problems, Kluwer, Dordrecht, Netherlands. P. Duchenes and B. Rémillard, eds. pp 141-159.
Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis. Second Edition. New York: Springer.
Clarkson, D. B., Fraley, C., Gu, C. C. and Ramsay, J. O. (2005). S+ Functional Data Analysis User's Guide. New York: Springer.
Ramsay, J. O. (2005). The control of behavioral input/output systems. In J. L. Schafe and T. A. Walls (Eds.), Models for Intensive Longitudinal Data. Oxford: Oxford University Press.
Lam, R. L. H. and Welch, W. J. (2004). Comparison of Methods Based on Diversity and Similarity for Molecule Selection and the Analysis of Drug Discovery Data. Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery. Ed. J Bajorath. NJ: Humana Press, pp. 301-315.
Bengio, Y., and Grandvalet Y. (2004). Bias in Estimating the Variance of K-Fold Cross-Validation. In Statistical Modeling and Analysis for Complex Data Problem, eds. P. Duchesne and B. Remillard, Kluwer publ, pp.75--95.
Moore, M., Froda, S. and Léger, C. (2003). Mathematical Statistics and Applications: Festchrift for Constance van Eeden. Lecture Notes - Monograph Series of the Institute of Mathematical Statistics, vol. 42, 495 pages.
Trentin E., Brugnara F., Bengio Y., Furlanello C. and De Mori R. (2002). Statistical and Neural Network Models for Speech Recognition. In Connectionist Approaches to Clinical Problems in Speech and Language, ed. R. Daniloff, Lawrence Erlbaum publ., p.213-264.
Ramsay, J. O. and Silverman, B. W. (2002). Functional Data Analysis. New York: Springer-Verlag.
Chipman, H. A., George, E.I. and McCulloch, R.E. (2002). Discussion of 'Recognition of Faces versus Greebles: A Case Study in Model Selection', by Viele, Kass, Tarr, Behrmann and Gauthier. In Case Studies in Bayesian Statistics, Volume 6, C Gatsonis, RE Kass, A Carriquiry, A Gelman, D Higdon, DK Pauler, I Verdinelli, Eds., Springer-Verlag, New York, NY, 124-129.
Dugas C., Bengio Y., Chapados N., Vincent P., Denoncourt G., Fournier C. (2002). Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking. In Intelligent Techniques for the Insurance Industry, World Scientific.
Chipman, H. A., George, E.I. and McCulloch, R.E. (2001). The Practical Implementation of Bayesian Model Selection. Chapter in Model Selection, P. Lahiri, Ed., Institute of Mathematical Statistics Lecture Notes - Monograph Series, V. 38, 65-134.
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (2001). Gradient-Based Learning Applied to Document Recognition. In Intelligent Signal Processing, chap. 9, ed. S. Haykin and B. Kosko, IEEE Press, pp.306-351.
Nadeau, C. and Bengio, Y. (2000). Inference for the Generalization Error. In Advances in Neural Information Processing Systems 12, pp.307-313.
Schmidhuber, J., Hochreiter, S. and Bengio, Y. (2000). Evaluating Benchmark Problems by Random Guessing. In Field Guide to Dynamical Recurrent Networks, ed. J. Kolen and S. Kremer, IEEE Press.
Hochreiter, S. and Bengio, Y. and Frasconi P. and Schmidhuber, J. (2000). Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies. In Field Guide to Dynamical Recurrent Networks, ed. J. Kolen and S. Kremer, IEEE Press.
Non-refereed contributions
Papers in non-refereed conference proceedings
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian and Yoshua Bengio (2010). Theano: a CPU and GPU Math Expression Compiler, in: Proceedings of the Python for Scientific Computing Conference (SciPy), Austin, TX.
Mosesova, S. Chipman, H. A., Mackay, R. J., and Steiner, S. (2002). Mining Functional Process Data. Spring Research Conference on Statistics in Industry and Technology, Ann Arbor, MI, and Fourth Biennial International Conference on Statistics, Probability and Related Areas DeKalb, IL.
Chen, V.C.P. and Welch, W. J. (2001). Statistical Methods for Deterministic Biomathematical Models. In Bulletin of the International Statistical Institute, 53rd Session Proceedings, 1, 397-400.
Theses
Nicolas Chapados (2010). Sequential Machine learning Approaches for Portfolio Management. Ph.D. thesis by Université de Montréal.
Stanislaus Lauly (2010). A model of expressive performance timing for the piano. Master thesis by Université of Montréal.
Sean Wood (2010). Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals. Master thesis by Université de Montréal.
Olivier Breuleux (2010). Échantillonnage dynamique de champs markoviens. Master thesis by Université de Montréal.
Francois Rivest (2009). Modèle informatique du coapprentissage des ganglions de la base et du cortex : L'apprentissage par renforcement et le développement de représentations. Ph.D. thesis by Université de Montréal.
Grégoire Mesnil (2009). Deep Learning with Noise and Sparsity constraints. Master thesis by École Normale Supérieure de Cachan.
Lysiane Bouchard (2009). Analyse par apprentissage automatique des réponses fMRI du cortex auditif à des modulations spectro-temporelles. Master thesis by Université de Montréal.
Isabelle Lajoie (2009). Apprentissage de représentations sur-complètes par entraînement d'auto-encodeurs. Master thesis by Université de Montréal.
Hugo Larochelle (2009). Étude de techniques d'apprentissage non-supervisé pour l'amélioration de l'entraînement supervisé de modèles connexionnistes, Phd Thesis by University of Montréal.
Tyler Lu (2009). Fundamental Limitations of Semi-Supervised Learning. May 2009. Master thesis by Univesity of Waterloo.
David Pal (2009). Contributions to Unsupervised and Semi-Supervised Learning. Phd Thesis by University of Waterloo.
Shujie Li (2009). Query classification based on a new query expansion approach, Master Thesis by Acadia University.
David Silver (2009). Reinforcement Learning and Simulation-Based Search in Computer Go, Phd Thesis by University of Alberta.
Kevin Waugh (2009). Abstraction in Large Extensive Games, Master Thesis by University of Alberta.
Wanhua Su (2008), Efficient Kernel Methods for Statistical Detection, PhD Thesis, University of Waterloo, Statistics and Actuarial Science.
Yuheng Wu (2007), Industrial Risk Classification using Credibility Theory and Hierarchical Clustering MSc Thesis, Acadia University, Mathematics and Statistics.
Nicolas Le Roux (2008), Avancées théoriques sur la représentation et l'optimisation des réseaux de neurones, PhD Thesis, Université de Montréal.
Ali Ghodsi (2007), Nonlinear Dimensionality Reduction with Side Information, PhD Thesis, University of Waterloo.
Dana Wilkinson (2007), Subjective Mapping, PhD Thesis, University of Waterloo.
Liangliang Wang (2007), Estimating nonlinear mixed-effects models by the generalized profiling method and its application to pharmacokinetics, Master thesis, 2007.
Tao Wang (2007), New Representations and Approximations for Sequential Decision Making under Uncertainty, Ph.D. Thesis, University of Alberta.
Yuhong Guo (2007), Learning Bayesian Networks from Data: Structure Optimization, and Parameter Estimation, PhD Thesis, University of Alberta.
Jiayuan Huang (2007), Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting, PhD thesis, University of Waterloo.
Linli Xu (2007), Convex Large Margin Training Techniques: Unsupervised, Semi-supervised, and Robust Support Vector Machines, Ph.D. Thesis, University of Waterloo.
David Campbell (2007), Bayesian Collocation Tempering and Generalized Profiling for Estimation of Parameters from Differential Equation Models, PhD Thesis, McGill University.
Mosesova, S. (2007), Flexible Mixed-Effect Modeling of Functional Data, with Applications to Process Monitoring, PhD thesis, University of Waterloo
Wang, X. (2007), Statistical Learning in Drug Discovery via Clustering and Mixtures, PhD thesis, University of Waterloo
Yuan, F. (2007). The comparison of three machine-learning algorithms for the screening of chemical compounds, MMath essay, University of Waterloo.
Zhang, Z. (2007). Customizing kernels in support vector machines, MMath thesis, University of Waterloo.
Carreau, Julie (2007). Modèles Pareto hybrides pour distributions asymétriques et à queues lourdes PhD Thesis, UdeM, 2007.
Pierre-Antoine Manzagol (2007). TONGA - Un algorithme de gradient naturel pour les problèmes de grande taille, Master Thesis, UdeM, 2007.
Alborz Geramifard (2007). iLSTD: incremental Least Squares Temporal Difference Learning, PhD Thesis, University of Alberta, 2007.
Cosmin Paduraru (2007). Planning with Approximate and Learned MDP Models, Master Thesis, University of Alberta, 2007.
Cao, J. (2006). Generalized Profiling Method and the Applications to Adaptive Penalized Smoothing, Generalized Semiparametric Additive Models and Estimating Differential Equations PhD Thesis, McGill University.
Erhan, D. (2006). Collaborative filtering techniques for drug discovery. M.Sc. Thesis, Université de Montréal.
Nahm, E. (2006). Classification models for transactional graph data. M.Sc. Thesis, Acadia University.
Fok, C. (2006). Techniques for the Analysis of Event Timings and Strengths. PhD Thesis, McGill University.
Ghodsi, A. (2005). Non-linear dimensionality reduction with side information. PhD Thesis, University of Waterloo.
Chen, W. (2005). Predicting rehabilitation potential with the K-nearest neighbors (KNN) algorithm. MMath Essay, University of Waterloo.
Zhou, Q. (2005). Sequential learning of SVMs for target identification in drug discovery. MMath Essay, University of Waterloo.
Wang, (M.) Y. (2005). Statistical Methods for High Throughput Screening Drug Discovery Data. PhD Thesis, University of Waterloo.
Boisvert, M. (2005). Réduction de dimension pour modèles graphiques probabilistes appliqués à la désambiguisation sémantique. M.Sc. Thesis, Université de Montréal.
Zhang, W. (2005). Functional data analysis for detecting structural boundaries of cortical area. M.Sc. Thesis, McGill University.
Koulis, T. (2005). Stochastic Population Dynamics Approaches to Sea Ice Modelling. PhD Thesis, University of Waterloo.
Cheng, Li. (2004). A Bayesian theory for forestry image understanding. PhD Thesis, University of Alberta.
Hooker, G. (2004). Diagnostics and Extrapolation in Machine Learning PhD Thesis, Department of Statistics, Stanford University.
Technical and internal reports
Frédéric Bastien, Yoshua Bengio, Arnaud Bergeron, Nicolas Boulanger-Lewandowski, Thomas Breuel, Youssouf Chherawala, Moustapha Cisse, Myriam Côté, Dumitru Erhan, Jeremy Eustache, Xavier Glorot, Xavier Muller, Sylvain Pannetier Lebeuf, Razvan Pascanu, Salah Rifai, François Savard and Guillaume Sicard (2010). Deep Self-Taught Learning for Handwritten Character Recognition, Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, number 1353.
Olivier Breuleux, Yoshua Bengio and Pascal Vincent (2010). Unlearning for Better Mixing, Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, number 1349.
Dumitru Erhan, Yoshua Bengio, Aaron Courville and Pascal Vincent (2009). Visualizing Higher-Layer Features of a Deep Network, Département d'Informatique et de Recherche Opérationnelle, University of Montreal, number 1341.
Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent and Olivier Delalleau (2009). Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines, Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, number 1345.
James Bergstra, Guillaume Desjardins, Pascal Lamblin and Yoshua Bengio (2009). Quadratic Polynomials Learn Better Image Features, Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, number 1337.
Yoshua Bengio, Jerome Louradour, Ronan Collobert and Jason Weston (2009). Curriculum Learning, Département d'informatique et recherche opérationnelle, Université de Montréal, number 1330.
Su, W., Chipman, H.A., and Zhu, M. (2008). "On the underestimation of model uncertainty by Bayesian K-nearest neighbors". Waterloo
Yoshua Bengio (2007). Learning deep architectures for AI. Technical Report 1312, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Yoshua Bengio and Olivier Delalleau (2007). Justifying and Generalizing Contrastive Divergence. Technical Report 1311, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Pascal Vincent, Hugo Larocholle,Yoshua Bengio and Pierre-Antoine Manzagol (2008). Extracting and Composing Robust Features with Denoising Autoencoders. Technical Report 1316, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Bouveyron, C. and Chipman, H. (2007), A supervised latent classifier for graph-structured data, technical report, Acadia University.
Nicolas Le Roux and Yoshua Bengio (2007). Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. Technical Report 1294, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Yoshua Bengio, Olivier Delalleau and Clarence Simard (2007). Decision Trees do not Generalize to New Variations. Technical Report 1304, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Nicolas Le Roux, Pierre-Antoine Manzagol and Yoshua Bengio (2007). Topmoumoute online natural gradient algorithm. Technical Report 1299, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Erhan, D., Bengio, Y., L'Heureux, P.J. and Yue, S.Y. (2006). Generalizing to a Zero-Data Task: a Computational Chemistry Case Study. Technical Report 1286, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Grandvalet, Y. and Bengio, Y. (2006) Hypothesis Testing for Cross-Validation. Technical Report 1285, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Larochelle, H. and Bengio, Y. (2006). Distributed Representation Prediction for Generalization to New Words. Technical Report 1284, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Carreau, J. and Bengio, Y. (2006). A Hybrid Pareto Model for Asymmetric Fat-Tail Data. Technical Report 1283, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Bengio, Y., Lamblin, P., Popovici, D. and Larochelle, H. (2006) Greedy Layer-Wise Training of Deep Networks. Technical Report 1282, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Le Roux, N. and Bengio, Y. (2006). Continuous Neural Networks. Technical Report 1281, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Bengio, Y., Delalleau, O. and Le Roux, N. (2005). The Curse of Dimensionality for Local Kernel Machines. Technical Report 1258, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Bengioy, Y. and Larochelle, H. (2005). Non-Local Manifold Parzen Windows. Technical Report 1264, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Bengio, Y., Le Roux, N., Vincent, P., Delalleau, O. and Marcotte, P. (2005). Convex Neural Networks. Technical Report 1263, Département d'Informatique et Recherche Opérationnelle, Université de Montréal.
Bellhouse, D. R., Chipman, H. A., and Stafford, J. E. (2001). Smoothing and additive models for survey data. Technical report.
Others reports
Zhu, M. (2008). How to draw a trilinear plot? . ASA Statistical Computing and Graphics Newsletter, *19*(1), 7 - 9.
