Jaime Carbonell

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Jaime Carbonell

Jaime Guillermo Carbonell,
an American computer scientist and AI researcher with focus on machine learning and machine translation. He holds a Ph.D. in computer science in 1979 from Yale University and is Allen Newell professor at Carnegie Mellon University, co-founder and chairman, of Carnegie Speech Incorporated and Wisdom Technologies Corporation . Jaime Carbonell invented multiple well-known algorithms and methods, including proactive machine learning for multi-source cost-sensitive active learning, linked conditional random fields (L-SCRF) for predicting tertiary and quaternary protein folds, maximal marginal relevance (MMR) for information novelty, retrieval and summarization, topic-conditioned modeling for novelty detection, symmetric optimal phrasal alignment method for trainable example-based and statistical machine translation, series-anomaly modeling for financial fraud detection and syndromic surveillance, knowledge-based interlingual machine translation, transformational analogy for case-based reasoning, derivational analogy for reconstructive justification-based reasoning, robust case-frame parsing, seeded version-space learning, and developed improvements to several other machine learning algorithms. Current research foci include robust statistical learning and mapping protein sequences to 3D structure and inferring functional properties, automated transfer-rule learning for machine translation, enriched active transfer learning context-based machine translation, and machine translation for very rare languages .

Selected Publications

1979

• Jaime Carbonell (1979). Subjective Understanding: Computer Models of Belief Systems. Ph.D. dissertation, Yale University

1980 ...

• Jaime Carbonell (1980). Learning and Problem Solving by Analogy. Preprints of the CMU Machine Learning Workshop-Symposium
• Jaime Carbonell (1981). Artificial Intelligence Research at Carnegie-Mellon University. AI Magazine, Vol. 2, No. 1
• Jaime Carbonell (1981). Counterplanning: A Strategy-Based Model of Adversary Planning in Real-World Situations. Artificial Intelligence, Vol. 16, [https://www.cs.cmu.edu/~jgc/publication/PublicationPDF/Counterplanning_a_StrategyBased_Model_of_Adversary_Planning_in_RealWorld_Situations_1981.pdf pdf}
• Jaime Carbonell (1982). Learning by Analogy. Technical report, Carnegie-Mellon University
• Ryszard Michalski, Jaime Carbonell, Tom Mitchell (1983). Machine Learning: An Artificial Intelligence Approach. Springer

Jaime Carbonell (1983). Learning by Analogy: Formulating and Generalizing Plans from Past Experience.

• Ryszard Michalski, Jaime Carbonell, Tom Mitchell (1985, 2014). Learning: An Artificial Intelligence Approach, Volume I. Morgan Kaufmann
• Ryszard Michalski, Jaime Carbonell, Tom Mitchell (1986). Machine Learning: An Artificial Intelligence Approach, Volume II. Morgan Kaufmann
• Tom Mitchell, Jaime Carbonell, Ryszard Michalski (1986). Machine Learning: A Guide to Current Research. The Kluwer International Series in Engineering and Computer Science, Vol. 12

1990 ...

• Peter Haddawy, Jaime Carbonell, Jörg H. Siekmann (eds.) (1994). Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action. Lecture Notes in Computer Science, Vol. 770, Springer
• Toru Ishida, Jörg H. Siekmann, Jaime Carbonell (1998). Community Computing and Support Systems: Social Interaction in Networked Communities. Lecture Notes in Computer Science, Vol. 1519, Springer
• Jaime Carbonell, Jade Goldstein (1998). The Use of MMR and Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR '98

2000 ...

• Jaime Carbonell, Yiming Yang, William W. Cohen (2000). Special Issue of Machine Learning on Information Retrieval Introduction. Machine Learning, Vol. 39, Nos. 2-3, pdf

2010 ...

• Xi Chen, Seyoung Kim, Qihang Lin, Jaime Carbonell, Eric P. Xing (2010). Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso. arXiv:1005.3579
• Xi Chen, Qihang Lin, Seyoung Kim, Jaime Carbonell, Eric P. Xing (2010). Smoothing proximal gradient method for general structured sparse regression. arXiv:1005.4717
• Adams Wei Yu, Lei Huang, Qihang Lin, Ruslan Salakhutdinov, Jaime Carbonell (2017). Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network. arXiv:1707.04822
• George Philipp, Jaime Carbonell (2017). Nonparametric Neural Networks. arXiv:1712.05440
• George Philipp, Dawn Song, Jaime Carbonell (2017). The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions. arXiv:1712.05577
• George Philipp, Jaime Carbonell (2018). The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks. arXiv:1806.00179

External Links

• Jaime Carbonell's Web Page
• Jaime Carbonell from Wikipedia
• The Mathematics Genealogy Project - Jaime Carbonell
• Jaime Carbonell - Google Scholar Citations

References

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