no code implementations • 7 Jul 2022 • William R. Zame
This paper demonstrates that, if we take terminal wealth constraints and self-financing constraints as seriously in the discrete model as in the continuous model, then the continuous trading model need not be the limit of discrete trading models.
no code implementations • 21 Feb 2022 • Alihan Hüyük, William R. Zame, Mihaela van der Schaar
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas.
1 code implementation • NeurIPS 2021 • Zhaozhi Qian, William R. Zame, Lucas M. Fleuren, Paul Elbers, Mihaela van der Schaar
To close this gap, we propose the latent hybridisation model (LHM) that integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system and to link the expert and latent variables to observable quantities.
no code implementations • 5 Oct 2018 • Onur Atan, William R. Zame, Mihaela van der Schaar
Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control).
no code implementations • 23 Feb 2018 • Onur Atan, William R. Zame, M. van der Schaar
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others.
1 code implementation • ICLR 2018 • Jinsung Yoon, William R. Zame, Mihaela van der Schaar
At runtime, the operator prescribes a performance level or a cost constraint, and Deep Sensing determines what measurements to take and what to infer from those measurements, and then issues predictions.
2 code implementations • 23 Nov 2017 • Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data).
no code implementations • 5 Jun 2017 • Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Our approach constructs a tree of subsets of the feature space and associates a predictor (predictive model) - determined by training one of a given family of base learners on an endogenously determined training set - to each node of the tree; we call the resulting object a tree of predictors.
no code implementations • 23 Dec 2016 • Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features.