no code implementations • 21 Aug 2023 • Jeong Min Lee, Milos Hauskrecht
Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time.
1 code implementation • 6 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.
no code implementations • 28 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.
1 code implementation • 5 Apr 2021 • Jeong Min Lee, Milos Hauskrecht
Clinical event sequences consist of thousands of clinical events that represent records of patient care in time.
1 code implementation • 19 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.
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.
no code implementations • 3 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.
no code implementations • 21 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.
no code implementations • 6 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?".
no code implementations • 28 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.
no code implementations • 15 May 2015 • Charmgil Hong, Milos Hauskrecht
Outlier detection aims to identify unusual data instances that deviate from expected patterns.
no code implementations • 6 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.
no code implementations • 16 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.
no code implementations • 14 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.
no code implementations • 13 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.
no code implementations • 27 Nov 2013 • Zitao Liu, Milos Hauskrecht
Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series.
no code implementations • 26 Sep 2013 • Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning.
no code implementations • 2 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.