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Found 16 papers, 12 papers with code
A Learning Based Hypothesis Test for Harmful Covariate Shift
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
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
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
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.
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
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).
Hierarchical Optimal Transport for Comparing Histopathology Datasets
Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels.
Mixture-of-experts VAEs can disregard variation in surjective multimodal data
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
Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology.
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
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.
Neural Pharmacodynamic State Space Modeling
Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression.
Clustering Interval-Censored Time-Series for Disease Phenotyping
In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping.
Variational Autoencoders for Collaborative Filtering
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 #4 on
Recommendation Systems
on Million Song Dataset
On the challenges of learning with inference networks on sparse, high-dimensional data
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network.
Structured Inference Networks for Nonlinear State Space Models
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 #4 on
Multivariate Time Series Forecasting
on USHCN-Daily
Deep Kalman Filters
Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.
Barrier Frank-Wolfe for Marginal Inference
We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope.
