Zeshan M Hussain — accepted AI-conference papers

2024

Benchmarking Observational Studies with Experimental Data under Right-Censoring

Ilker Demirel, Edward De Brouwer, Zeshan M Hussain, Michael Oberst, Anthony A Philippakis, David Sontag

AISTATS 2024poster

Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major limitation of existing procedures is not accounting for cen…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2022

Using time-series privileged information for provably efficient learning of prediction models

Rickard K.A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David Sontag, Fredrik Johansson

AISTATS 2022poster

We study prediction of future outcomes with supervised models that use privileged information during learning. The privileged information comprises samples of time series observed between the baseline time of prediction and the future outcome; this information is only available at training time whic…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2021

Neural Pharmacodynamic State Space Modeling

Zeshan M Hussain, Rahul G. Krishnan, David Sontag

ICML 2021spotlight

Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, existing neural network based approaches that learn representations of patient state, while very flexible, are susceptible to overfitting. We propose a deep generative mo…

Cited by 16 Source ↗PDF ↗code ↗Scholar ↗