136 citations · 357 across the 20 of their papers we have counts for
21 papers
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…
Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding
Augustin Toma, Patrick R. Lawler, Jimmy Ba +3
We present Clinical Camel, an open large language model (LLM) explicitly tailored for clinical research. Fine-tuned from LLaMA-2 using QLoRA, Clinical Camel achieves state-of-the-a…
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…
A Learning Based Hypothesis Test for Harmful Covariate Shift
Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan
The ability to quickly and accurately identify covariate shift at test time is a critical and often overlooked component of safe machine learning systems deployed in high-risk doma…
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…
HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
Weiming Ren, Ruijing Zeng, Tongzi Wu +2
There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians…
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Richard J. Chen, Chengkuan Chen, Yicong Li +4
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for lo…
Hierarchical Optimal Transport for Comparing Histopathology Datasets
Anna Yeaton, Rahul G. Krishnan, Rebecca Mieloszyk +2
Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels. Transfer learning allows researchers to overcom…
Mixture-of-experts VAEs can disregard variation in surjective multimodal data
Jannik Wolff, Tassilo Klein, Moin Nabi +2
Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthc…
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
Richard J. Chen, Rahul G. Krishnan
Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology. However…
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
Rickard K. A. Karlsson, Martin Willbo, Zeshan Hussain +3
We study prediction of future outcomes with supervised models that use privileged information during learning. The privileged information comprises samples of time series observed…
Neural Pharmacodynamic State Space Modeling
Zeshan Hussain, Rahul G. Krishnan, David Sontag
Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, existing neural network based approaches that lear…
Clustering Interval-Censored Time-Series for Disease Phenotyping
Irene Y. Chen, Rahul G. Krishnan, David Sontag
Unsupervised learning is often used to uncover clusters in data. However, different kinds of noise may impede the discovery of useful patterns from real-world time-series data. In…
Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman +1
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacit…
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul G. Krishnan, Dawen Liang, Matthew Hoffman
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such mode…
Structured Inference Networks for Nonlinear State Space Models
Rahul G. Krishnan, Uri Shalit, David Sontag
Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form,…
Deep Kalman Filters
Rahul G. Krishnan, Uri Shalit, David Sontag
Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy wide…
Barrier Frank-Wolfe for Marginal Inference
Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope. The algorithm is based on the conditional gr…