2006 - Fellow of Alfred P. Sloan Foundation
Carlos Guestrin mainly focuses on Artificial intelligence, Machine learning, Mathematical optimization, Wireless sensor network and Submodular set function. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Sketch, Scalability and Sparse matrix. His studies deal with areas such as Optimization problem, Data mining, Asynchronous communication and Data consistency as well as Machine learning.
Carlos Guestrin combines subjects such as Fault tolerance and Representation with his study of Data mining. His study in the field of Linear programming is also linked to topics like Gaussian process. Carlos Guestrin interconnects Distributed computing, Probabilistic logic and Approximation algorithm in the investigation of issues within Wireless sensor network.
Artificial intelligence, Machine learning, Mathematical optimization, Wireless sensor network and Theoretical computer science are his primary areas of study. Carlos Guestrin studies Artificial intelligence, focusing on Deep learning in particular. His Machine learning research is multidisciplinary, incorporating perspectives in Probabilistic logic and Asynchronous communication.
His work on Submodular set function as part of general Mathematical optimization study is frequently linked to Gaussian process, therefore connecting diverse disciplines of science. The Wireless sensor network study combines topics in areas such as Distributed computing, Data mining, Approximation algorithm, Node and Optimization problem. His research in Theoretical computer science intersects with topics in Computation, Inference, Graph and Parallel computing.
Carlos Guestrin mainly investigates Artificial intelligence, Deep learning, Machine learning, Computer architecture and Algorithm. He has researched Artificial intelligence in several fields, including Natural language processing, Generator, Debugging, Flexibility and Pattern recognition. He focuses mostly in the field of Deep learning, narrowing it down to matters related to CUDA and, in some cases, Hardware acceleration, Scalability and Operator.
His biological study spans a wide range of topics, including Question answering, Isolation and Measure. The concepts of his Computer architecture study are interwoven with issues in Software and Compiler. The various areas that he examines in his Algorithm study include Heuristics, Support vector machine and Scale invariance.
His primary areas of investigation include Artificial intelligence, Deep learning, Field-programmable gate array, Software portability and CAS latency. His Artificial intelligence study combines topics in areas such as Graph, Operator, Computer engineering, Natural language processing and CUDA. His studies in Natural language processing integrate themes in fields like Adversarial system, Debugging and Flexibility.
His CUDA research incorporates themes from Hardware acceleration and Scalability. The study incorporates disciplines such as Convolution and Matrix multiplication in addition to Deep learning. His biological study spans a wide range of topics, including Computer architecture, Optimizing compiler, Compiler, Code and End-to-end principle.
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.
XGBoost: A Scalable Tree Boosting System
Tianqi Chen;Carlos Guestrin.
knowledge discovery and data mining (2016)
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Marco Túlio Ribeiro;Sameer Singh;Carlos Guestrin.
north american chapter of the association for computational linguistics (2016)
Cost-effective outbreak detection in networks
Jure Leskovec;Andreas Krause;Carlos Guestrin;Christos Faloutsos.
knowledge discovery and data mining (2007)
PowerGraph: distributed graph-parallel computation on natural graphs
Joseph E. Gonzalez;Yucheng Low;Haijie Gu;Danny Bickson.
operating systems design and implementation (2012)
Max-Margin Markov Networks
Ben Taskar;Carlos Guestrin;Daphne Koller.
neural information processing systems (2003)
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
Andreas Krause;Ajit Singh;Carlos Guestrin.
Journal of Machine Learning Research (2008)
Model-driven data acquisition in sensor networks
Amol Deshpande;Carlos Guestrin;Samuel R. Madden;Joseph M. Hellerstein.
very large data bases (2004)
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Yucheng Low;Danny Bickson;Joseph Gonzalez;Carlos Guestrin.
very large data bases (2012)
GraphChi: large-scale graph computation on just a PC
Aapo Kyrola;Guy Blelloch;Carlos Guestrin.
operating systems design and implementation (2012)
Anchors: High-Precision Model-Agnostic Explanations
Marco Tulio Ribeiro;Sameer Singh;Carlos Guestrin.
national conference on artificial intelligence (2018)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: