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Empirical Inference Proceedings Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI) Biester, L., Demszky, D., Jin, Z., Sachan, M., Tetreault, J., Wilson, S., Xiao, L., Zhao, J. Association for Computational Linguistics, December 2022 (Published) URL BibTeX
Empirical Inference Proceedings Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022) Schölkopf, B., Uhler, C., Zhang, K. 177, Proceedings of Machine Learning Research, PMLR, April 2022 (Published) URL BibTeX
Empirical Inference Proceedings Proceedings of the 1st Workshop on NLP for Positive Impact Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C. Association for Computational Linguistics, August 2021 (Published) URL BibTeX
Empirical Inference Proceedings Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI) Ihler, A. T., Janzing, D. 869 pages, AUAI Press, June 2016 (Published) URL BibTeX
Empirical Inference Proceedings Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24 Deisenroth, M., Szepesvári, C., Peters, J. 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 Web BibTeX
Empirical Inference Proceedings MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication) Panagiotaki, E., O’Donnell, L., Schultz, T., Zhang, G. 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2012), Workshop on Computational Diffusion MRI , 2012 PDF BibTeX
Empirical Inference Proceedings Machine Learning and Interpretation in Neuroimaging - Revised Selected and Invited Contributions Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. 266, Springer, Heidelberg, Germany, International Workshop, MLINI 2011, Held at NIPS, 2012, Lecture Notes in Computer Science, Vol. 7263 DOI BibTeX
Empirical Inference Proceedings JMLR Workshop and Conference Proceedings Volume 19: COLT 2011 Kakade, S., von Luxburg, U. 834, MIT Press, Cambridge, MA, USA, 24th Annual Conference on Learning Theory , June 2011 Web BibTeX
Empirical Inference Proceedings JMLR Workshop and Conference Proceedings: Volume 6 Guyon, I., Janzing, D., Schölkopf, B. 288, MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS 2008 Workshop) , February 2010 Web BibTeX
Empirical Inference Proceedings CogRob 2008: The 6th International Cognitive Robotics Workshop Lespérance, Y., Lakemeyer, G., Peters, J., Pirri, F. Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), 35, Patras University Press, Patras, Greece, 6th International Cognitive Robotics Workshop (CogRob 2008), July 2008 Web BibTeX
Empirical Inference Proceedings Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference Schölkopf, B., Platt, J., Hofmann, T. Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), 1690, MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS 2006), September 2007
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.
Web BibTeX
Empirical Inference Proceedings Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference Weiss, Y., Schölkopf, B., Platt, J. Proceedings of the 19th Annual Conference on Neural Information Processing Systems (NIPS 2005), 1676, MIT Press, Cambridge, MA, USA, 19th Annual Conference on Neural Information Processing Systems (NIPS 2005), May 2006
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2005 meeting, held in Vancouver.
Web BibTeX
Empirical Inference Proceedings Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment Quinonero Candela, J., Dagan, I., Magnini, B., Lauria, F. Proceedings of the First Pascal Machine Learning Challenges Workshop on Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment (MLCW 2005), 462, Lecture Notes in Computer Science, Springer, Heidelberg, Germany, First Pascal Machine Learning Challenges Workshop (MLCW 2005), 2006
This book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005. The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; the second challenge was to recognize objects from a number of visual object classes in realistic scenes; the third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Web DOI BibTeX
Empirical Inference Proceedings Advanced Lectures on Machine Learning Bousquet, O., von Luxburg, U., Rätsch, G. ML Summer Schools 2003, LNAI 3176:240, Springer, Berlin, Germany, ML Summer Schools, September 2004
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in T{\"u}bingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
Web BibTeX
Empirical Inference Proceedings Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175 Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B. Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 Web DOI BibTeX
Empirical Inference Proceedings Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference Thrun, S., Saul, L., Schölkopf, B. Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS 2003), June 2004
The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.
Web BibTeX
Empirical Inference Proceedings Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), LNCS Vol. 2777 Schölkopf, B., Warmuth, M. Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), COLT/Kernel 2003, 746, Springer, Berlin, Germany, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, November 2003, Lecture Notes in Computer Science ; 2777 DOI BibTeX