2017 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the fields of machine learning, computational biology and natural language processing.
2002 - Fellow of Alfred P. Sloan Foundation
Tommi S. Jaakkola mainly focuses on Algorithm, Artificial intelligence, Mathematical optimization, Machine learning and Linear programming relaxation. His research integrates issues of Spline, Theoretical computer science, Cluster analysis and Reinforcement learning in his study of Algorithm. The Artificial intelligence study combines topics in areas such as Margin, Natural language processing and Pattern recognition.
Tommi S. Jaakkola combines subjects such as Markov decision process, State, Variational message passing and Applied mathematics with his study of Mathematical optimization. His Machine learning research includes themes of Variable-order Bayesian network and Data mining. His Linear programming relaxation research incorporates themes from Graphical model, Inference and Coordinate descent.
Artificial intelligence, Algorithm, Machine learning, Theoretical computer science and Inference are his primary areas of study. His Artificial intelligence research integrates issues from Natural language processing, Task and Pattern recognition. His Algorithm course of study focuses on Graphical model and Mathematical optimization.
His study explores the link between Machine learning and topics such as Data mining that cross with problems in Cluster analysis. His research investigates the connection between Theoretical computer science and topics such as Graph that intersect with problems in Graph. His research links Bayesian network with Inference.
His primary areas of study are Theoretical computer science, Artificial intelligence, Machine learning, Graph and Algorithm. Tommi S. Jaakkola has researched Theoretical computer science in several fields, including Decision tree, Equivalence, Representation and Constant function. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Simple, Encoder and Pattern recognition.
His Machine learning research is multidisciplinary, incorporating perspectives in Variation and Inference. His Graph study combines topics from a wide range of disciplines, such as Artificial neural network, Recurrent neural network, Graph, Generative grammar and Convolutional neural network. His research in Algorithm intersects with topics in Linear approximation and Relaxation.
The scientist’s investigation covers issues in Theoretical computer science, Artificial intelligence, Graph, Machine learning and Generative grammar. His Theoretical computer science research incorporates elements of Autoregressive model and Drug discovery. His Artificial intelligence research is multidisciplinary, incorporating elements of Workflow, Complement and Natural language processing.
His work on Convolutional neural network is typically connected to Patent literature, Rationalization and Counterfactual thinking as part of general Machine learning study, connecting several disciplines of science. His Convolutional neural network study combines topics in areas such as Artificial neural network and Computer graphics. His work on Generative model as part of general Generative grammar research is often related to Novelty and Vocabulary, thus linking different fields of science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
An introduction to variational methods for graphical models
Michael I. Jordan;Zoubin Ghahramani;Tommi S. Jaakkola;Lawrence K. Saul.
Machine Learning (1999)
Exploiting Generative Models in Discriminative Classifiers
Tommi Jaakkola;David Haussler.
neural information processing systems (1998)
Maximum-Margin Matrix Factorization
Nathan Srebro;Jason Rennie;Tommi S. Jaakkola.
neural information processing systems (2004)
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Tommi Jaakkola;Michael I. Jordan;Satinder P. Singh.
neural information processing systems (1993)
Weighted low-rank approximations
Nathan Srebro;Tommi Jaakkola.
international conference on machine learning (2003)
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms
Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári.
Machine Learning (2000)
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
J. Zico Kolter;Tommi S. Jaakkola.
international conference on artificial intelligence and statistics (2012)
MAP estimation via agreement on trees: message-passing and linear programming
M.J. Wainwright;T.S. Jaakkola;A.S. Willsky.
IEEE Transactions on Information Theory (2005)
Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle
Itamar Simon;John Barnett;Nancy Hannett;Christopher T Harbison.
Computational discovery of gene modules and regulatory networks.
Ziv Bar-Joseph;Georg K Gerber;Tong Ihn Lee;Nicola J Rinaldi.
Nature Biotechnology (2003)
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