What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
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.
His most cited work include:
- XGBoost: A Scalable Tree Boosting System (5832 citations)
- "Why Should I Trust You?": Explaining the Predictions of Any Classifier (3743 citations)
- Cost-effective outbreak detection in networks (1703 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (32.16%)
- Machine learning (20.70%)
- Mathematical optimization (18.94%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (32.16%)
- Deep learning (3.96%)
- Machine learning (20.70%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Anchors: High-Precision Model-Agnostic Explanations. (481 citations)
- TVM: an automated end-to-end optimizing compiler for deep learning (276 citations)
- Semantically Equivalent Adversarial Rules for Debugging NLP models (252 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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.
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