Search Results for author: Milos Hauskrecht

Found 18 papers, 3 papers with code

Personalized Event Prediction for Electronic Health Records

no code implementations21 Aug 2023 Jeong Min Lee, Milos Hauskrecht

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

1 code implementation6 Apr 2022 Jeong Min Lee, Milos Hauskrecht

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

no code implementations28 Jun 2021 Matthew Barren, Milos Hauskrecht

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.

Multi-Task Learning Representation Learning

Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning

1 code implementation5 Apr 2021 Jeong Min Lee, Milos Hauskrecht

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time.

Event Outlier Detection in Continuous Time

1 code implementation19 Dec 2019 Si-Qi Liu, Milos Hauskrecht

In this work, we study and develop methods for detecting outliers in continuous-time event sequences, including unexpected absence and unexpected occurrences of events.

Outlier Detection Point Processes +1

Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes

no code implementations NeurIPS 2019 Si-Qi Liu, Milos Hauskrecht

``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.

Gaussian Processes Point Processes

Detection of Abnormal Input-Output Associations

no code implementations3 Aug 2017 Charmgil Hong, Si-Qi Liu, Milos Hauskrecht

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.

Outlier Detection Relation

Detecting Unusual Input-Output Associations in Multivariate Conditional Data

no code implementations21 Dec 2016 Charmgil Hong, Milos Hauskrecht

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.

Outlier Detection

Active Perceptual Similarity Modeling with Auxiliary Information

no code implementations6 Nov 2015 Eric Heim, Matthew Berger, Lee Seversky, Milos Hauskrecht

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?".

Active Learning

Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels

no code implementations28 Jul 2015 Eric Heim, Milos Hauskrecht

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.

Metric Learning

MCODE: Multivariate Conditional Outlier Detection

no code implementations15 May 2015 Charmgil Hong, Milos Hauskrecht

Outlier detection aims to identify unusual data instances that deviate from expected patterns.

General Classification Outlier Detection

Efficient Online Relative Comparison Kernel Learning

no code implementations6 Jan 2015 Eric Heim, Matthew Berger, Lee M. Seversky, Milos Hauskrecht

Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search.

Collaborative Filtering Retrieval

A Mixtures-of-Experts Framework for Multi-Label Classification

no code implementations16 Sep 2014 Charmgil Hong, Iyad Batal, Milos Hauskrecht

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.

Classification General Classification +1

Binary Classifier Calibration: Non-parametric approach

no code implementations14 Jan 2014 Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht

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.

Classifier calibration Decision Making +1

Binary Classifier Calibration: Bayesian Non-Parametric Approach

no code implementations13 Jan 2014 Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht

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.

BIG-bench Machine Learning Binary Classification +1

Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series

no code implementations27 Nov 2013 Zitao Liu, Milos Hauskrecht

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series.

Time Series Time Series Analysis

The Bregman Variational Dual-Tree Framework

no code implementations26 Sep 2013 Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht

Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning.

Text Categorization

Relative Comparison Kernel Learning with Auxiliary Kernels

no code implementations2 Sep 2013 Eric Heim, Hamed Valizadegan, Milos Hauskrecht

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.

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