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Found 9 papers, 3 papers with code
Asset Trading in Continuous Time: A Cautionary Tale
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.
Inferring Lexicographically-Ordered Rewards from Preferences
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas.
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
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.
Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation
Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control).
Learning Optimal Policies from Observational Data
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others.
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks
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.
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks
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).
ToPs: Ensemble Learning with Trees of Predictors
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.
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
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.
