Search Results for author: Rahul G. Krishnan

Found 26 papers, 19 papers with code

InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation

1 code implementation1 Jun 2024 Jacob Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan

To remedy this, we propose InterpreTabNet, a variant of the TabNet model that models the attention mechanism as a latent variable sampled from a Gumbel-Softmax distribution.

feature selection Language Modelling +2

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

1 code implementation10 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.

Decision Making Meta Reinforcement Learning +3

A Geometric Explanation of the Likelihood OOD Detection Paradox

1 code implementation27 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.

Order-based Structure Learning with Normalizing Flows

2 code implementations14 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.

Causal Discovery

Copula-Based Deep Survival Models for Dependent Censoring

no code implementations20 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).

Survival Analysis Survival Prediction

DuETT: Dual Event Time Transformer for Electronic Health Records

1 code implementation25 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.

Time Series

Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections

1 code implementation3 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.

Time Series Time Series Analysis

A Learning Based Hypothesis Test for Harmful Covariate Shift

2 code implementations6 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.

Learning predictive checklists from continuous medical data

no code implementations14 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.

Partial Identification of Treatment Effects with Implicit Generative Models

1 code implementation14 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.

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

1 code implementation3 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.

Medical Code Prediction

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

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).

Self-Supervised Learning Survival Prediction

Hierarchical Optimal Transport for Comparing Histopathology Datasets

no code implementations18 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.

Transfer Learning Type prediction

Mixture-of-experts VAEs can disregard variation in surjective multimodal data

no code implementations11 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.

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology

1 code implementation1 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.

Knowledge Distillation Transfer Learning

Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

1 code implementation28 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.

Time Series Time Series Analysis

Neural Pharmacodynamic State Space Modeling

2 code implementations22 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.

Time Series Time Series Analysis

Clustering Interval-Censored Time-Series for Disease Phenotyping

no code implementations13 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.

Clustering Time Series +1

Variational Autoencoders for Collaborative Filtering

18 code implementations16 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.

Bayesian Inference Collaborative Filtering +2

On the challenges of learning with inference networks on sparse, high-dimensional data

1 code implementation17 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.

Variational Inference

Structured Inference Networks for Nonlinear State Space Models

3 code implementations30 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.

Multivariate Time Series Forecasting

Deep Kalman Filters

3 code implementations16 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.

counterfactual Counterfactual Inference +1

Barrier Frank-Wolfe for Marginal Inference

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.

Variational Inference

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