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08/14/2023
OCDaf: Ordered Causal Discovery with Autoregressive Flows
We propose OCDaf, a novel order-based method for learning causal graphs ...
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06/20/2023
Copula-Based Deep Survival Models for Dependent Censoring
A survival dataset describes a set of instances (e.g. patients) and prov...
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05/19/2023
Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding
Large Language Models (LLMs) present immense potential in the medical fi...
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04/25/2023
DuETT: Dual Event Time Transformer for Electronic Health Records
Electronic health records (EHRs) recorded in hospital settings typically...
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03/03/2023
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Neural ordinary differential equations (Neural ODEs) are an effective fr...
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12/06/2022
A Learning Based Hypothesis Test for Harmful Covariate Shift
The ability to quickly and accurately identify covariate shift at test t...
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11/14/2022
Learning predictive checklists from continuous medical data
Checklists, while being only recently introduced in the medical domain, ...
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10/14/2022
Partial Identification of Treatment Effects with Implicit Generative Models
We consider the problem of partial identification, the estimation of bou...
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08/03/2022
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
There are several opportunities for automation in healthcare that can im...
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06/06/2022
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Vision Transformers (ViTs) and their multi-scale and hierarchical variat...
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04/18/2022
Hierarchical Optimal Transport for Comparing Histopathology Datasets
Scarcity of labeled histopathology data limits the applicability of deep...
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04/11/2022
Mixture-of-experts VAEs can disregard variation in surjective multimodal data
Machine learning systems are often deployed in domains that entail data ...
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03/01/2022
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
Tissue phenotyping is a fundamental task in learning objective character...
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10/28/2021
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
We study prediction of future outcomes with supervised models that use p...
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02/22/2021
Neural Pharmacodynamic State Space Modeling
Modeling the time-series of high-dimensional, longitudinal data is impor...
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02/13/2021
Clustering Left-Censored Multivariate Time-Series
Unsupervised learning seeks to uncover patterns in data. However, differ...
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02/16/2018
Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for...
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10/17/2017
On the challenges of learning with inference networks on sparse, high-dimensional data
We study parameter estimation in Nonlinear Factor Analysis (NFA) where t...
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09/30/2016
Structured Inference Networks for Nonlinear State Space Models
Gaussian state space models have been used for decades as generative mod...
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11/16/2015
Deep Kalman Filters
Kalman Filters are one of the most influential models of time-varying ph...
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11/06/2015