44 citations · 82 across the 7 of their papers we have counts for
8 papers · 1 filter
Order-based Structure Learning with Normalizing Flows
Hamidreza Kamkari, Vahid Balazadeh, Vahid Zehtab +1
Estimating the causal structure of observational data is a challenging combinatorial search problem that scales super-exponentially with graph size. Existing methods use continuous…
Copula-Based Deep Survival Models for Dependent Censoring
Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner +1
A survival dataset describes a set of instances (e.g. patients) and provides, for each, either the time until an event (e.g. death), or the censoring time (e.g. when lost to follow…
DuETT: Dual Event Time Transformer for Electronic Health Records
Alex Labach, Aslesha Pokhrel, Xiao Shi Huang +5
Electronic health records (EHRs) recorded in hospital settings typically contain a wide range of numeric time series data that is characterized by high sparsity and irregular obser…
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Edward De Brouwer, Rahul G. Krishnan
Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a con…
Learning predictive checklists from continuous medical data
Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan
Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpre…
Partial Identification of Treatment Effects with Implicit Generative Models
Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan
We consider the problem of partial identification, the estimation of bounds on the treatment effects from observational data. Although studied using discrete treatment variables or…