M
Mihaela van der Schaar
Researcher at University of Cambridge
Publications - 794
Citations - 15280
Mihaela van der Schaar is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Markov decision process. The author has an hindex of 50, co-authored 719 publications receiving 11300 citations. Previous affiliations of Mihaela van der Schaar include University of Miami & University of California, Berkeley.
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Journal ArticleDOI
Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study.
TL;DR: Evidence of two distinct but associated effects is found: increased mortality in the north region (regional effect) and in the Pardo and Black populations (ethnicity effect).
Proceedings Article
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
TL;DR: This paper investigates a method for ensuring (differential) privacy of the generator of the Generative Adversarial Nets (GAN) framework, and modifies the Private Aggregation of Teacher Ensembles (PATE) framework and applies it to GANs.
Proceedings Article
Time-series Generative Adversarial Networks
TL;DR: A novel framework for generating realistic time-series data that combines the flexibility of the unsupervised paradigm with the control afforded by supervised training is proposed, which consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.
Proceedings Article
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
TL;DR: A very different approach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly and achieves large and statistically significant performance improvements over previous state-of-the-art methods.
Proceedings ArticleDOI
Reputation-based incentive protocols in crowdsourcing applications
Yu Zhang,Mihaela van der Schaar +1 more
TL;DR: It is proved that the proposed incentives protocol can make the website operate close to Pareto efficiency, and also examines an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol.