Search Results for author:
Found 18 papers, 3 papers with code
Personalized Event Prediction for Electronic Health Records
Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time.
Learning to Adapt Clinical Sequences with Residual Mixture of Experts
In this work, we aim to alleviate this limitation by refining a one-fits-all model using a Mixture-of-Experts (MoE) architecture.
Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning
More specifically, our method improves prediction performance of a low-prior task by jointly optimizing a shared model that combines the loss of the target event and a broad range of generic clinical events.
Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning
Clinical event sequences consist of thousands of clinical events that represent records of patient care in time.
Event Outlier Detection in Continuous Time
In this work, we study and develop methods for detecting outliers in continuous-time event sequences, including unexpected absence and unexpected occurrences of events.
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
``Regressive point processes'' refer to point processes that directly model the dependency between an event and any past event, an example of which is a Hawkes process.
Detection of Abnormal Input-Output Associations
We study a novel outlier detection problem that aims to identify abnormal input-output associations in data, whose instances consist of multi-dimensional input (context) and output (responses) pairs.
Detecting Unusual Input-Output Associations in Multivariate Conditional Data
We present a novel outlier detection framework that identifies abnormal input-output associations in data with the help of a decomposable conditional probabilistic model that is learned from all data instances.
Active Perceptual Similarity Modeling with Auxiliary Information
A common way to learn such a model is from relative comparisons in the form of triplets: responses to queries of the form "Is object a more similar to b than it is to c?".
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels
Finally, we perform qualitative assessments on the metrics learned by CAMEL and show that they identify and clearly articulate important factors in how the model performs inference.
MCODE: Multivariate Conditional Outlier Detection
Outlier detection aims to identify unusual data instances that deviate from expected patterns.
Efficient Online Relative Comparison Kernel Learning
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search.
A Mixtures-of-Experts Framework for Multi-Label Classification
We develop a novel probabilistic approach for multi-label classification that is based on the mixtures-of-experts architecture combined with recently introduced conditional tree-structured Bayesian networks.
Binary Classifier Calibration: Non-parametric approach
We prove three theorems showing that using a simple histogram binning post-processing method, it is possible to make a classifier be well calibrated while retaining its discrimination capability.
Binary Classifier Calibration: Bayesian Non-Parametric Approach
A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time.
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series.
The Bregman Variational Dual-Tree Framework
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning.
Relative Comparison Kernel Learning with Auxiliary Kernels
In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects.
