Her primary areas of investigation include Artificial intelligence, Machine learning, Scalability, Computer network and Wireless. The various areas that she examines in her Artificial intelligence study include Data mining, Missing data, Imputation and Computer vision. Her Machine learning research incorporates elements of Counterfactual thinking, Generative grammar, Risk assessment and Past history.
As a part of the same scientific family, Mihaela van der Schaar mostly works in the field of Scalability, focusing on Coding and, on occasion, Reference frame, Quality of service and Multimedia. Adaptation and Real-time communication is closely connected to The Internet in her research, which is encompassed under the umbrella topic of Computer network. Her study in the field of Wireless network and Physical layer also crosses realms of Mobile telephony.
Mihaela van der Schaar mainly focuses on Artificial intelligence, Machine learning, Computer network, Distributed computing and Mathematical optimization. She has researched Artificial intelligence in several fields, including Scalability, Data mining and Computer vision. Mihaela van der Schaar studies Regret, a branch of Machine learning.
Her work in Computer network addresses subjects such as Wireless, which are connected to disciplines such as Communication channel. Her work in Distributed computing is not limited to one particular discipline; it also encompasses Markov decision process. A large part of her Mathematical optimization studies is devoted to Nash equilibrium.
Her main research concerns Artificial intelligence, Machine learning, Counterfactual thinking, Inference and Observational study. Much of her study explores Artificial intelligence relationship to Function. Her studies deal with areas such as Variety and Bayesian probability as well as Machine learning.
Her Observational study research is multidisciplinary, incorporating perspectives in Domain, Causal inference and Confounding. The study incorporates disciplines such as Gold standard, Randomized controlled trial and Experimental data in addition to Causal inference. Her Feature course of study focuses on Transplantation and Matching.
Mihaela van der Schaar mostly deals with Machine learning, Artificial intelligence, Intensive care, Observational study and Ethnic group. Her study in Machine learning is interdisciplinary in nature, drawing from both Variety, Clinical decision support system and Counterfactual thinking. Mihaela van der Schaar combines subjects such as Structure, Sample, Key and Benchmark with her study of Counterfactual thinking.
Her research investigates the link between Artificial intelligence and topics such as Counterfactual conditional that cross with problems in Inference. Her Intensive care research integrates issues from Capacity planning, Interface, Decision support system and Scale. Her Observational study research incorporates themes from Causal inference and Confounding.
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.
Combined MPEG-4 FGS and modulation algorithm for wireless video transmission
Joseph Meehan;Der Schaar Mihaela Van.
Reputation-based incentive protocols in crowdsourcing applications
Yu Zhang;Mihaela van der Schaar.
international conference on computer communications (2012)
In-band motion compensated temporal filtering
Yiannis Andreopoulos;Adrian Munteanu;Joeri Barbarien;Mihaela Van der Schaar.
Signal Processing-image Communication (2004)
System and method for fine granular scalable video with selective quality enhancement
Chen Yingwei;Radha Hayder;Van Der Schaar Mihaela.
Context-Aware Proactive Content Caching With Service Differentiation in Wireless Networks
Sabrina Muller;Onur Atan;Mihaela van der Schaar;Anja Klein.
IEEE Transactions on Wireless Communications (2017)
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon;James Jordon;Mihaela van der Schaar.
international conference on machine learning (2018)
Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.
Ahmed M. Alaa;Thomas Bolton;Thomas Bolton;Emanuele Di Angelantonio;Emanuele Di Angelantonio;James H. F. Rudd.
PLOS ONE (2019)
Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning
Byung-Gook Kim;Yu Zhang;Mihaela van der Schaar;Jang-Won Lee.
IEEE Transactions on Smart Grid (2016)
Multimedia Over IP and Wireless Networks: Compression, Networking, and Systems
Mihaela van der Schaar;Philip A. Chou.
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
Changhee Lee;William R. Zame;Jinsung Yoon;Mihaela van der Schaar.
national conference on artificial intelligence (2018)
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