no code implementations • 13 Feb 2021 • Irene Y. Chen, Rahul G. Krishnan, David Sontag
In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping.
no code implementations • 11 Apr 2022 • Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima
Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthcare.
no code implementations • 18 Apr 2022 • Anna Yeaton, Rahul G. Krishnan, Rebecca Mieloszyk, David Alvarez-Melis, Grace Huynh
Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels.
no code implementations • 14 Nov 2022 • 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 interpretability.
no code implementations • 20 Jun 2023 • Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan
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-up - which is a lower bound on the time until the event).
no code implementations • 30 Nov 2023 • Linfeng Du, Ji Xin, Alex Labach, Saba Zuberi, Maksims Volkovs, Rahul G. Krishnan
Transformer-based models have greatly pushed the boundaries of time series forecasting recently.
no code implementations • 12 Feb 2024 • Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
Scheduling laboratory tests for ICU patients presents a significant challenge.
1 code implementation • 27 Mar 2024 • Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
We also show that this scenario can be identified through local intrinsic dimension (LID) estimation, and propose a method for OOD detection which pairs the likelihoods and LID estimates obtained from a pre-trained DGM.
1 code implementation • 10 Apr 2024 • Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis
We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information.
1 code implementation • NeurIPS 2015 • 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.
1 code implementation • 28 Oct 2021 • Rickard K. A. Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David Sontag, Fredrik D. Johansson
Our question is when using this privileged data leads to more sample-efficient learning of models that use only baseline data for predictions at test time.
1 code implementation • 3 Mar 2023 • Edward De Brouwer, Rahul G. Krishnan
These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update the latent representation of the dynamical system.
2 code implementations • 14 Aug 2023 • Hamidreza Kamkari, Vahid Balazadeh, Vahid Zehtab, Rahul G. Krishnan
Estimating the causal structure of observational data is a challenging combinatorial search problem that scales super-exponentially with graph size.
1 code implementation • 14 Oct 2022 • Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan
We propose a new method for partial identification of average treatment effects(ATEs) in general causal graphs using implicit generative models comprising continuous and discrete random variables.
2 code implementations • 22 Feb 2021 • Zeshan Hussain, Rahul G. Krishnan, David Sontag
Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression.
1 code implementation • 3 Aug 2022 • Weiming Ren, Ruijing Zeng, Tongzi Wu, Tianshu Zhu, Rahul G. Krishnan
One of the challenges in curriculum learning is the design of curricula -- i. e., in the sequential design of tasks that gradually increase in difficulty.
2 code implementations • 6 Dec 2022 • 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 domains.
1 code implementation • 17 Oct 2017 • 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.
1 code implementation • 25 Apr 2023 • Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G. Krishnan
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 observations.
1 code implementation • 19 May 2023 • Augustin Toma, Patrick R. Lawler, Jimmy Ba, Rahul G. Krishnan, Barry B. Rubin, Bo wang
We present Clinical Camel, an open large language model (LLM) explicitly tailored for clinical research.
1 code implementation • 1 Mar 2022 • 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.
3 code implementations • 30 Sep 2016 • Rahul G. Krishnan, Uri Shalit, David Sontag
We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.
Ranked #5 on Multivariate Time Series Forecasting on USHCN-Daily
3 code implementations • 16 Nov 2015 • Rahul G. Krishnan, Uri Shalit, David Sontag
Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.
2 code implementations • CVPR 2022 • Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood
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 low-resolution images (e. g. - 256x256, 384384).
18 code implementations • 16 Feb 2018 • Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
Ranked #5 on Recommendation Systems on Million Song Dataset