Publications récentes

Publications récentes

Publications récentes (depuis 2012)


Note: la page web personnelle des chercheurs peuvent contenir des publications plus récentes.
Cliquez sur l’icône pour accéder au DOI (Digital Object Identifier).

    Membres du laboratoire

  • Belkacem Abdous (Université Laval)
  • Jean-François Angers (Université de Montréal)
  • Masoud Asgharian (McGill University)
  • Mylène Bédard (Université de Montréal)
  • Yoshua Bengio (Université de Montréal)
  • Taoufik Bouezmarni (Université de Sherbrooke)
  • Alexandre Bureau (Université Laval)
  • Félix Camirand Lemyre (Université de Sherbrooke)
  • Anne-Sophie Charest (Université Laval)
  • Yogendra P. Chaubey (Concordia University)
  • Fateh Chebana (INRS)
  • Ting-Huei Chen (Université Laval)
  • Jean-François Coeurjolly (Université du Québec à Montréal)
  • Pierre Duchesne (Université de Montréal)
  • Thierry Duchesne (Université Laval)
  • Debbie J. Dupuis (HEC Montréal)
  • Sorana Froda (UQÀM)
  • Christian Genest (McGill University)
  • Simon Guillotte (UQÀM)
  • David Haziza (Université de Montréal)
  • Khader Khadraoui (Université Laval)
  • Abbas Khalili (McGill University)
  • Aurélie Labbe (HEC Montréal)
  • Lajmi Lakhal Chaieb (Université Laval)
  • Fabrice Larribe (Université du Québec à Montréal)
  • Geneviève Lefebvre (UQÀM)
  • Christian Léger (Université de Montréal)
  • Brenda MacGibbon (Université du Québec à Montréal)
  • Éric Marchand (Université de Sherbrooke)
  • Erica E. M. Moodie (McGill University)
  • Alejandro Murua (Université de Montréal)
  • Johanna Nešlehová (McGill University)
  • Karim Oualkacha (Université du Québec à Montréal)
  • Vahid Partovi Nia (Polytechnique Montréal )
  • François Perron (Université de Montréal)
  • Robert W. Platt (McGill University)
  • James O. Ramsay (McGill University)
  • Bruno Rémillard (HEC Montréal)
  • Louis-Paul Rivest (Université Laval)
  • Paramita Saha Chaudhuri (McGill University)
  • Alexandra M. Schmidt (McGill University)
  • Mireille Schnitzer (Université de Montréal)
  • Arusharka Sen (Concordia University)
  • Russell Steele (McGill University)
  • David A. Stephens (McGill University)
  • Denis Talbot (Université Laval)
  • Yi Yang (McGill University)

  • Postdocs du laboratoire

  • Juliana Schulz (McGill University)


  • Belkacem Abdous



    Jean-François Angers



    Masoud Asgharian

    Chapitres de livres:

    • Asgharian, M., Wolfson, C., Wolfson, D. B., Analysis of biased survival data: The Canadian Study of Health and Aging and beyond, in Statistics in Action: A Candian Outlook, J. F. Lawless, d., Boca Raton, FL, CRC Press, 2014.



    Mylène Bédard



    Yoshua Bengio

    Monographies / Livres:

    • Goodfellow, I., Bengio, Y., Courville, A., Deep Learning, Adaptive Computation and Machine Learning Series, Cambridge, MA, MIT Press, 2016.

    Chapitres de livres:

    • Bengio, Y., Courville, A., Deep learning of representations, in Handbook on Neural Information Processing, M. Bianchini, M. Maggini, L. C. Jain, d., Intelligent Systems Reference Library, Vol. 49, Heidelberg, Springer, 2013.
    • Bengio, Y., Evolving culture versus local minima , in Growing Adaptive Machines, T. Kowaliw, N. Bredeche, R. Doursat, d., Studies in Computational Intelligence Vol. 557, Springer, 2013.
    • Bengio, Y., Practical recommendations for gradient-based trining of deep architectures, in Neural Networks: Tricks of the Trade, G. Montavon, G. B. Orr, K.-R. Mller, d., Lecture Notes in Computer Science, Vol. 7700, Berlin, Springer, 2012.

    Rapports de recherche:

    • Glehre, ., Moczulski, M., Visin, F., Bengio, Y., Mollifying networks, arXiv:1608.04980, aot 2016.
    • Ahn, S., Choi, H., Pnnamaa, T., Bengio, Y., A neural knowledge language model, arXiv:1608.00318, aot 2016.
    • Ott, J., Lin, Z., Zhang, Y., Liu, S.-C., Bengio, Y., Recurrent neural networks with limited numerical precision, arXiv:1608.06902, aot 2016.
    • Bahdanau, D., Brakel, P., Xu, K., Goyal, A., Lowe, R., Pineau, J., Courville, A., Bengio, Y., An actor-critic algorithm for sequence prediction, arXiv:1607.07086, juillet 2016.
    • Havaei, M., Guizard, N., Chapados, N., Bengio, Y., HeMIS: Hetero-modal image segmentation, arXiv:1607.05194, juillet 2016.
    • Choi, H., Cho, K., Bengio, Y., Context-dependent word representation for neural machine translation, arXiv:1607.00578, juillet 2016.
    • Wu, Y., Zhang, S., Zhang, Y., Bengio, Y., Salakhutdinov, R., On multiplicative integration with recurrent neural networks, arXiv:1606.06630, juin 2016.
    • Zhang, X.-Y., Yin, F., Zhang, Y.-M., Liu, C.-L., Bengio, Y., Drawing and recognizing Chinese characters with recurrent neural network, arXiv:1606.06539, juin 2016.
    • Kim, T., Bengio, Y., Deep directed generative models with energy-based probability estimation, arXiv:1606.03439, juin 2016.
    • Sordoni, A., Bachman, P., Bengio, Y., Iterative alternating neural attention for machine reading, arXiv:1606.02245, juin 2016.
    • Bengio, Y., Scellier, B., Bilaniuk, O., Sacramento, J., Senn, W., Feedforward initialization for fast inference of deep generative networks is biologically plausible, arXiv:1606.01651, juin 2016.
    • Krueger, D., Maharaj, T., Kramr, J., Pezeshki, M., Ballas, N., Ke, N., Goyal, A., Bengio, Y., Larochelle, H., Courville, A., Pal, C., Zoneout: Regularizing RNNs by randomly preserving hidden activations, arXiv:1606.01305, juin 2016.
    • Serban, I. V., Klinger, T., Tesauro, G., Talamadupula, K., Zhou, B., Bengio, Y., Courville, A., Multiresolution recurrent neural networks: An application to dialogue response generation, arXiv:1606.00776, juin 2016.
    • Chandar, S., Ahn, S., Larochelle, H., Vincent, P., Tesauro, G., Bengio, Y., Hierarchical memory networks, arXiv:1605.07427, mai 2016.
    • Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Bengio, Y., Bergeron, A., Bergstra, J., Bisson, V., Bleecher Snyder, J., Bouchard, N., Boulanger-Lewandowski, N., Bouthillier, X., de Brbisson, A., Breuleux, O., Carrier, P.-L., Cho, K., Chorowski, J., Christiano, P., Cooijmans, T., Ct, M.-A., Ct, M., Courville, A., Dauphin, Y., Delalleau, O., Demouth, J., Desjardins, G., Dieleman, S., Dinh, L., Ducoffe, M., Dumoulin, V., Kahou, S. E., Erhan, D., Firat, O., Germain, M., Glorot, X., Goodfellow, I., Graham, M., Glehre, ., Hamel, P., Harlouchet, I., Heng, J.-P., Hidasi, B., Honari, S., Jain, A., Jean, S., Jia, K., Korobov, M., Kulkarni, V., Lamb, A., Lamblin, P., Larsen, E., Laurent, C., Lee, S., Lefranois, S., Lemieux, S., Lonard, N., Lin, Z., Livezey, J. A., Lorenz, C., Lowin, J., Ma, Q., Manzagol, P.-A., Mastropietro, O., McGibbon, R. T., Memisevic, R., van Merrinboer, B., Michalski, V., Mirza, M., Orlandi, A., Pal, C., Pascanu, R., Pezeshki, M., Raffel, C., Renshaw, D., Rocklin, M., Romero, A., Roth, M., Sadowski, P., Salvatier, J., Savard, F., Schlter, J., Schulman, J., Schwartz, G., Serban, I. V., Serdyuk, D., Shabanian, S., Simon, ., Spieckermann, S., Subramanyam, S. R., Sygnowski, J., Tanguay, J., van Tulder, G., Turian, J., Urban, S., Vincent, P., Visin, F., de Vries, H., Warde-Farley, D., Webb, D. J., Willson, M., Xu, K., Xue, L., Yao, L., Zhang, S., Zhang, Y., Theano: A Python framework for fast computation of mathematical expressions, arXiv:1605.02688, mai 2016.
    • Glehre, ., Ahn, S., Nallapati, R., Zhou, B., Bengio, Y., Pointing the unknown words, arXiv:1603.08148, mars 2016.
    • Serban, I. V., Garca-Durn, A., Glehre, ., Ahn, S., Chandar, S., Courville, A., Bengio, Y., Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus, arXiv:1603.06807, mars 2016.
    • Chung, J., Cho, K., Bengio, Y., A character-level decoder without explicit segmentation for neural machine translation, arXiv:1603.06147, mars 2016.
    • Zhang, S., Wu, Y., Che, T., Lin, Z., Memisevic, R., Salakhutdinov, R., Bengio, Y., Architectural complexity measures of recurrent neural networks, arXiv:1602.08210, fvrier 2016.
    • Scellier, B., Bengio, Y., Towards a biologically plausible backprop, arXiv:1602.05179, fvrier 2016.
    • Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y., Binarized neural networks: Training deep neural networks with weights and activations constrained to $+1$ or $-1$, arXiv:1602.02830, fvrier 2016.
    • Courbariaux, M., Bengio, Y., David, J.-P., Training deep neural networks with low precision multiplications, arXiv:1412.7024, dcembre 2015. ICLR 2015 Workshop Poster Session
    • Im, D. J., Ahn, S., Memisevic, R., Bengio, Y., Denoising criterion for variational auto-encoding framework, arXiv:1511.06406, novembre 2015.
    • Visin, F., Ciccone, M., Romero, A., Kastner, K., Cho, K., Bengio, Y., Matteucci, M., Courville, A., ReSeg: A recurrent neural metwork-based model for semantic segmentation, arXiv:1511.07053, novembre 2015. CVPR Deep Vision Workshop, 2016
    • Alain, G., Lamb, A., Sankar, C., Courville, A., Bengio, Y., Variance reduction in SGD by distributed importance sampling, arXiv:1511.06481, novembre 2015. International Conference on Learning Representations 2016
    • Arjovsky, M., Shah, A., Bengio, Y., Unitary evolution recurrent neural networks, arXiv:1511.06464, novembre 2015.
    • Bahdanau, D., Serdyuk, D., Brakel, P., Ke, N., Chorowski, J., Courville, A., Bengio, Y., Task loss estimation for sequence prediction, arXiv:1511.06456, novembre 2015.
    • Yao, L., Ballas, N., Cho, K., Smith, J. R., Bengio, Y., Empirical performance upper bounds for image and video captioning, arXiv:1511.04590, novembre 2015. ICLR 2016
    • Lin, Z., Courbariaux, M., Memisevic, R., Bengio, Y., Neural networks with few multiplications, arXiv:1510.03009, octobre 2015. 4th International Conference on Learning Representations — ICLR 2016 (San Juan, PR, 2016)
    • Bengio, Y., Fischer, A., Early inference in energy-based models approximates back-propagation, arXiv:1510.02777, octobre 2015.
    • Laurent, C., Pereyra, G., Brakel, P., Zhang, Y., Bengio, Y., Batch normalized recurrent neural networks, arXiv:1510.01378, octobre 2015. ICASSP 2016
    • Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., Wu, Y., STDP as presynaptic activity times rate of change of postsynaptic activity, arXiv:1509.05936, septembre 2015.
    • de Brbisson, A., Simon, ., Auvolat, A., Vincent, P., Bengio, Y., Artificial neural networks applied to taxi destination prediction, arXiv:1508.00021, aot 2015.
    • Serban, I. V., Sordoni, A., Bengio, Y., Courville, A., Pineau, J., Hierarchical neural network generative models for movie dialogues, arXiv:1507.04808, juillet 2015.
    • Auvolat, A., Chandar, S., Vincent, P., Larochelle, H., Bengio, Y., Clustering is efficient for approximate maximum inner product search, arXiv:1507.05910, juillet 2015.
    • van Merrinboer, B., Bahdanau, D., Dumoulin, V., Serdyuk, D., Warde-Farley, D., Chorowski, J., Bengio, Y., Blocks and fuel: Frameworks for deep learning, arXiv:1506.00619, juin 2015.
    • Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A., Bengio, Y., ReNet: A recurrent neural network based alternative to convolutional networks, arXiv:1505.00393, mai 2015.
    • Glehre, ., Firat, O., Xu, K., Cho, K., Barrault, L., Lin, H.-C., Bougares, F., Schwenk, H., Bengio, Y., On using monolingual corpora in neural machine translation, arXiv:1503.03535, mars 2015.
    • Bengio, Y., Lee, D.-H., Bornschein, J., Mesnard, T., Lin, Z., Towards biologically plausible deep learning, arXiv:1502.04156, fvrier 2015.
    • Glehre, ., Moczulski, M., Bengio, Y., ADASECANT: Robust adaptive secant method for stochastic gradient, arXiv:1412.7419 dcembre 2014. OPT 2015
    • Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., Bengio, Y., FitNets: Hints for thin deep nets, arXiv:1412.6550, dcembre 2014.
    • Hill, F., Cho, K., Jean, S., Devin, C., Bengio, Y., Embedding word similarity with neural machine translation, arXiv:1412.6448, dcembre 2014.
    • Mesnil, G., Mikolov, T., Ranzato, M. A., Bengio, Y., Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews, arXiv:1412.5335, dcembre 2014. ICLR 2015
    • Chung, J., Glehre, ., Cho, K., Bengio, Y., Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv:1412.3555, dcembre 2014. NIPS 2014 Deep Learning and Representation Learning Workshop (Poster)
    • Chorowski, J., Bahdanau, D., Cho, K., Bengio, Y., End-to-end continuous speech recognition using attention-based recurrent NN: First results, arXiv:1412.1602, dcembre 2014. NIPS 2014 Deep Learning and Representation Learning Workshop (Poster)
    • Dinh, L., Krueger, D., Bengio, Y., NICE: Non-linear independent components estimation, arXiv:1410.8516, octobre 2014. ICLR 2015
    • Hill, F., Cho, K., Jean, S., Devin, C., Bengio, Y., Not all neural embeddings are born equal, arXiv:1410.0718, octobre 2014. NIPS 2014 Workshop on Learning Semantics
    • Ozair, S., Bengio, Y., Deep directed generative autoencoders, arXiv:1410.0630, octobre 2014.
    • Desjardins, G., Luo, H., Courville, A., Bengio, Y., Deep tempering, arXiv:1410.0123, octobre 2014.
    • Cho, K., van Merrinboer, B., Bahdanau, D., Bengio, Y., On the properties of neural machine translation: Encoder-decoder aspproaches, arXiv:1409.1259, septembre 2014. 8th Workshop on Syntax, Semantics and Structure in Statistical Translation
    • Pouget-Abadie, J., Bahdanau, D., van Merrinboer, B., Cho, K., Bengio, Y., Overcoming the curse of sentence length for neural machine translation using automatic segmentation, arXiv:1409.1257, septembre 2014. 8th Workshop on Syntax, Semantics and Structure in Statistical Translation
    • Bahdanau, D., Cho, K., Bengio, Y., Neural machine translation by jointly learning to align and translate, arXiv:1409.0473, septembre 2014. ICLR 2015
    • Bengio, Y., How auto-encoders could provide credit assignment in deep networks via target propagation, arXiv:1407.7906, juillet 2014.
    • Cho, K., Bengio, Y., Exponentially increasing the capacity-to-computation ratio for conditional computation in deep learning, arXiv:1406.7362, juin 2014.
    • Bornschein, J., Bengio, Y., Reweighted wake-sleep, arXiv:1406.2751, juin 2014. ICLR 2015
    • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., Generative adversarial networks, arXiv:1406.2661, juin 2014.
    • Cho, K., van Merrinboer, B., Glehre, ., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv:1406.1078, juin 2014. EMNLP 2014
    • Pascanu, R., Dauphin, Y., Ganguli, S., Bengio, Y., On the saddle point problem for non-convex optimization, arXiv:1405.4604, mai 2014.
    • Goodfellow, I., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., Bengio, Y., Pylearn2: A machine learning research library, arXiv:1308.4214, aot 2013.
    • Bengio, Y., Lonard, N., Courville, A., Estimating or propagating gradients through stochastic neurons for conditional computation, arXiv:1308.3432, aot 2013.
    • Bengio, Y., Estimating or propagating gradients through stochastic neurons, arXiv:1305.2982, mai 2013.
    • Delalleau, O., Courville, A., Bengio, Y., Efficient EM training of Gaussian mixtures with missing data, arXiv:1209.0521, septembre 2012.
    • Bengio, Y., Alain, G., Rifai, S., Implicit density estimation by local moment matching to sample from auto-encoders, arXiv:1207.0057, juillet 2012.
    • Desjardins, G., Courville, A., Bengio, Y., On training deep Boltzmann machines, arXiv:1203.4416, mars 2012.



    Taoufik Bouezmarni



    Alexandre Bureau



    Félix Camirand Lemyre



    Anne-Sophie Charest

    Chapitres de livres:

    • Charest, A.-S., Empirical evaluation of statistical inference from differentially-private contingency tables, in Privacy in Statistical Databases, Josep Domingo-Ferrer, Ilenia Tinnirello, d., Lecture Notes in Computer Science, Vol. 7556, Berlin, Springer, 2012. UNESCO Chair in Data Privacy, International Conference, PSD 2012, Palermo, Italy, September 26-28, 2012. Proceedings



    Yogendra P. Chaubey

    Monographies / Livres:

    • Chaubey, Y. P., Some recent advances in mathematics and statistics, Singapore, World Sci. Publ., 2013.
    • Chaubey, Y. P., Adhikari, A., Adhikari, M. R., Contemporary topics in mathematics & statistics with applications 1, Vol. 1, India, New Delhi, Asian Books Private Limited, 2012.

    Chapitres de livres:

    • Howlader, T., Rahman, M., Chaubey, Y. P., On wavelet-based methods for noise reduction of cDNA microarray images, in Mathematical and Statistical Applications in Life Sciences and Engineering, A. Adhikari, M. R. Adhikari, Y. P. Chaubey, d., Singapore, Springer, 2017.
    • Chaubey, Y. P., Li, J., Asymmetric kernel density estimator for length biased data, in Contemporary topics in mathematics and statistics with applications, Avishek Adhikari, Mahima Ranjan Adhikari, Yogendra P. Chaubey, d., Vol. 1, New Delhi, India, Asian Books, 2013.



    Fateh Chebana

    Chapitres de livres:

    • Genest, C., Chebana, F., Copula modeling in hydrologic frequency analysis, in Handbook of Applied Hydrology, V. P. Singh, d., New York, McGraw-Hill, 2017.
    • Chebana, F., Multivariate analysis of hydrological variables, in Encyclopedia of Environmetrics, A. H. El-Shaarawi, W. W. Piegorsch, d., Wiley, 2013.



    Ting-Huei Chen



    Jean-François Coeurjolly



    Pierre Duchesne

    Rapports de recherche:

    • Duchesne, P., Lafaye de Micheaux, P., Tagne Tatsinkou, J. F., On strong consistency and asymptotic normality of one-step Gauss–Newton estimators in ARMA time series models, Centre de recherches mathmatiques, CRM-3371, janvier 2019.



    Thierry Duchesne

    Rapports de recherche:

    • Duchesne, T., Rmillard, B., Marcotte, O., Septime atelier de rsolution de problmes industriels de Montral / Seventh Montral Industrial Problem Solving Workshop, Centre de recherches mathmatiques, CRM-3358, avril 2017.



    Debbie J. Dupuis



    Sorana Froda



    Christian Genest

    Monographies / Livres:

    • Lin, X., Genest, C., Banks, D., Molenberghs, G., Scott, D. W., Wang, J.-L. (EDT), Past, present, and future of statistical science, London, Chapman and Hall, 2014.
    • Reid, N. M., Adem, A., Bierstone, E., Campbell, E., Dean, C. R., Genest, C., Kamran, N., Kuske, R., Lewis, M., Ivanoff, G., Thompson, A.-M., Solutions for a Complex Age: Long Range Plan for Mathematical and Statistical Science Researchin Canada: 2013-2018, 2012.

    Chapitres de livres:

    • Genest, C., Chebana, F., Copula modeling in hydrologic frequency analysis, in Handbook of Applied Hydrology, V. P. Singh, d., New York, McGraw-Hill, 2017.
    • Genest, C., Nešlehov, J., Modeling dependence beyond correlation, in Statistics in Action: A Canadian Outllook, J. F. Lawless, d., Boca Raton FL, CRC Press, 2014.
    • Genest, C., Nešlehov, J., Copulas and copula models, in Encyclopedia of Environmetrics, Second Edition, Abdel H. El-Shaarawi Walter W. Piegorsch, d., Chichester, John Wiley & Sons, Ltd, 2013.
    • Genest, C., Nešlehov, J., Copula modeling for extremes, in Encyclopedia of Environmetrics, Second Edition, Abdel H. El-Shaarawi Walter W. Piegorsch, d., Chichester, John Wiley & Sons, Ltd, 2013.



    Simon Guillotte



    David Haziza

    Rapports de recherche:

    • Chaput, H., Chauvet, G., Haziza, D., Salembier, L., Solard, J., Joint imputation procedures for categorical variables, arXiv:1511.00990, novembre 2015.
    • Valle, A.-A., Haziza, D., Variance estimation in the presence of imputed data for high entropy sampling designs, Universit de Neuchtel, 2014.



    Khader Khadraoui



    Abbas Khalili

    Chapitres de livres:

    • Khalili, A., Chen, J., Stephens, D. A., Regularization in regime-switching Gaussian autoregressive models, in Advanced Statistical Methods in Data Science, D.-G. Chen, J. Chen, X. Lu, G. Yi, H. Yu, d., ICSA Book Series in Statistics, Singapore, Springer, 2016.



    Aurélie Labbe

    Chapitres de livres:

    • Labbe, A., Huang, L. O., Infante-Rivard, C., Transmission ratio distortion: A neglected phenomenon with many consequences in genetic analysis and population genetics, in Epigenetics and Complex Traits, Anna K. Naumova, Celia M.T. Greenwood, d., New York, Springer, 2013.



    Lajmi Lakhal Chaieb



    Fabrice Larribe

    Rapports de recherche:

    • Larribe, F., Descary, M.-H., DMap: a coalescent methodology for genetic mapping of a complex trait, UQAM, 2014, 30 p.



    Geneviève Lefebvre



    Christian Léger



    Brenda MacGibbon



    Éric Marchand

    Monographies / Livres:

    • Fourdrinier, D., Marchand, ., Rukhin, A. L. (EDT), Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman 8, IMS Collections, Vol. 8, Beachwood, OH, Inst. Math. Stat., 2012.

    Chapitres de livres:

    • Marchand, ., Jafari Jozani, M., Tripathi, Y. M., Inadmissible estimators of normal quantiles and two-sample problems with additional information, in Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman, D. Fourdrinier, Marchand, A. L. Rukhin, d., IMS Collections, Vol. 8, Beachwood, OH, Inst. Math. Stat., 2012.

    Rapports de recherche:

    • Kubokawa, T., Marchand, ., Strawderman, W. E., On predictive density estimation for location families under integrated $L_2$ and $L_1$ losses, arXiv:1408.5297, aot 2014.



    Erica E. M. Moodie

    Monographies / Livres:

    • Kosorok, M. R., Moodie, E. E. M. (EDT), Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine, Philadelphia, PA, SIAM, 2015.
    • Chakraborty, B., Moodie, E. E. M., Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine 76, Statistics for Biology and Health, Vol. 76, Springer, 2013.
    • Chakraborty, B., Moodie, E. E. M., Statistical Methods for Dynamic Treatment Regimes 76, Statistics for Biology and Health, Vol. 76, Springer, 2013.

    Chapitres de livres:

    • Schnitzer, M., van der Laan, M., Moodie, E. E. M., Platt, R. W., LTMLE with clustering, in Targeted Learning in Data Science, Springer Series in Statistics, Vol. 15, Cham, Springer, 2018.
    • Moodie, E. E. M., Stephens, D. A., Dynamic treatment regimes, in Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, J. L. Teugels, d., American Cancer Society, 2018.
    • Moodie, E. E. M., Stephens, D. A., Wallace, M., G-estimation, in Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, J. L. Teugels, d., American Cancer Society, 2018.
    • Wallace, M., Moodie, E. E. M., Analysis in the single-stage setting: an overview of estimation approaches for dynamic treatment regimes, in Adaptive Treatment Strategies in Practice, M. R. Kosorok, E. E. M. Moodie, d., Philadelphia, PA, SIAM, 2015.



    Alejandro Murua



    Johanna Nešlehová

    Monographies / Livres:

    • Cramer, E., Nešlehov, J., Vorkurs Mathematik: Arbeitsbuch zum Studienbeginn in Bachelor-Studiengngen 5, Vol. 5, Verlag, Berlin, Springer, 2012.

    Chapitres de livres:

    • Genest, C., Nešlehov, J., Modeling dependence beyond correlation, in Statistics in Action: A Canadian Outllook, J. F. Lawless, d., Boca Raton FL, CRC Press, 2014.
    • Genest, C., Nešlehov, J., Copulas and copula models, in Encyclopedia of Environmetrics, Second Edition, Abdel H. El-Shaarawi Walter W. Piegorsch, d., Chichester, John Wiley & Sons, Ltd, 2013.
    • Genest, C., Nešlehov, J., Copula modeling for extremes, in Encyclopedia of Environmetrics, Second Edition, Abdel H. El-Shaarawi Walter W. Piegorsch, d., Chichester, John Wiley & Sons, Ltd, 2013.



    Karim Oualkacha



    Vahid Partovi Nia



    François Perron



    Robert W. Platt

    Chapitres de livres:

    • Schnitzer, M., van der Laan, M., Moodie, E. E. M., Platt, R. W., LTMLE with clustering, in Targeted Learning in Data Science, Springer Series in Statistics, Vol. 15, Cham, Springer, 2018.



    James O. Ramsay

    Monographies / Livres:

    • Ramsay, J. O., Hooker, G., Dynamic Data Analysis, Springer Series in Statistics, New York, Springer, 2017.

    Chapitres de livres:

    • Ramsay, J. O., Hermanussen, M., Watching children grow taught us all we know, in Statistics in Action: A Candian Outlook, J. F. Lawless, d., Boca Raton, FL, CRC Press, 2014.



    Bruno Rémillard

    Monographies / Livres:

    • Rmillard, B., Statistical Methods for Financial Engineering, Boca Raton, FL, CRC Press, 2013.

    Chapitres de livres:

    • Rmillard, B., Statistics in financial engineering, in Statistics in Action: A Candian Outlook, J. F. Lawless, d., Boca Raton, FL, CRC Press, 2014.

    Rapports de recherche:

    • Duchesne, T., Rmillard, B., Marcotte, O., Septime atelier de rsolution de problmes industriels de Montral / Seventh Montral Industrial Problem Solving Workshop, Centre de recherches mathmatiques, CRM-3358, avril 2017.
    • Rmillard, B., Non-parametric change problems using multipliers, SSRN, 2043632, avril 2012.
    • Rmillard, B., Specification for dynamic models using multipliers, SSRN, 2028558, mars 2012.



    Louis-Paul Rivest

    Chapitres de livres:

    • Rivest, L.-P., Baillargeon, S., Capture-recapture methods for estimating the size of a population: dealing with variable capture probabilities, in Statistics in Action: A Canadian Outlook, J. F. Lawless, d., Boca Raton FL, CRC Press, 2014.



    Paramita Saha Chaudhuri



    Alexandra M. Schmidt

    Chapitres de livres:

    • Lopes, H. F., Schmidt, A. M., Dynamic models, in Handbook of Environmental and Ecological Statistics, A. E. Gelfand, M. Fuentes, J. A. Hoeting, R. L. Smith, d., Chapman & Hall/CRC Handbooks of Modern Statistical Methods, Boca Raton, FL, CRC Press, 2018.



    Mireille Schnitzer

    Chapitres de livres:

    • Schnitzer, M., van der Laan, M., Moodie, E. E. M., Platt, R. W., LTMLE with clustering, in Targeted Learning in Data Science, Springer Series in Statistics, Vol. 15, Cham, Springer, 2018.



    Arusharka Sen



    Russell Steele



    David A. Stephens

    Monographies / Livres:

    • Damien, P., Dellaportas, P., Polson, N. G., Stephens, D. A. (EDT), Bayesian Theory and Apllications, Oxford University Press, 2013.

    Chapitres de livres:

    • Moodie, E. E. M., Stephens, D. A., Dynamic treatment regimes, in Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, J. L. Teugels, d., American Cancer Society, 2018.
    • Moodie, E. E. M., Stephens, D. A., Wallace, M., G-estimation, in Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, J. L. Teugels, d., American Cancer Society, 2018.
    • Khalili, A., Chen, J., Stephens, D. A., Regularization in regime-switching Gaussian autoregressive models, in Advanced Statistical Methods in Data Science, D.-G. Chen, J. Chen, X. Lu, G. Yi, H. Yu, d., ICSA Book Series in Statistics, Singapore, Springer, 2016.
    • Stephens, D. A., G-estimation for dynamic treatment regimes in the longitudinal setting, in Adaptive Treatment Strategies in Practice, M. R. Kosorok, E. E. M. Moodie, d., ASA-SIAM Series on Statistics and Applied Mathematics, Philadelphia, PA, SIAM, 2016.
    • Griffin, J. E., Stephens, D. A., Advances in Markov chain Monrte Carlo, in Bayesian Theory and Applications, P. Damien, P. Dellaportas, N. G. Polson, D. A. Stephens, d., Oxford, Oxford Univ. Press, 2013.
    • Stephens, D. A., Complexity in systems level biology and genetics: statistical perspectives, in Computational Complexity. Vols. 1–6, R. A. Meyers, d., New York, Springer, 2012.



    Denis Talbot



    Yi Yang

    Chapitres de livres:

    • Righi, M. B., Yang, Y., Ceretta, P. S., Nonparametric expectile regression for conditional autoregressive expected shortfall estimation, in Risk Management Post Financial Crisis: A Period of Monetary Easing, J. A. Batten, N. F. Wagner, d., Contemporary Studies in Economic and Financial Analysis, Vol. 96, Bingley, Emerald Group Publishing Limited, 2014.

    Rapports de recherche:

    • Mai, Q., Yang, Y., Zou, H., Multiclass sparse discriminant analysis, arXiv:1504.05845, avril 2015.



    Juliana Schulz