Search Results for author: Hugh Chen

Found 12 papers, 2 papers with code

Contrastive Corpus Attribution for Explaining Representations

no code implementations30 Sep 2022 Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee

To address this, we propose contrastive corpus similarity, a novel and semantically meaningful scalar explanation output based on a reference corpus and a contrasting foil set of samples.

Contrastive Learning Object Localization

Algorithms to estimate Shapley value feature attributions

1 code implementation15 Jul 2022 Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee

Based on the various feature removal approaches, we describe the multiple types of Shapley value feature attributions and methods to calculate each one.

Explaining a Series of Models by Propagating Shapley Values

no code implementations30 Apr 2021 Hugh Chen, Scott M. Lundberg, Su-In Lee

Local feature attribution methods are increasingly used to explain complex machine learning models.

Mortality Prediction

True to the Model or True to the Data?

no code implementations29 Jun 2020 Hugh Chen, Joseph D. Janizek, Scott Lundberg, Su-In Lee

Furthermore, we argue that the choice comes down to whether it is desirable to be true to the model or true to the data.

BIG-bench Machine Learning

Forecasting adverse surgical events using self-supervised transfer learning for physiological signals

no code implementations12 Feb 2020 Hugh Chen, Scott Lundberg, Gabe Erion, Jerry H. Kim, Su-In Lee

Here, we present a transferable embedding method (i. e., a method to transform time series signals into input features for predictive machine learning models) named PHASE (PHysiologicAl Signal Embeddings) that enables us to more accurately forecast adverse surgical outcomes based on physiological signals.

Time Series Transfer Learning

Explaining Models by Propagating Shapley Values of Local Components

no code implementations27 Nov 2019 Hugh Chen, Scott Lundberg, Su-In Lee

In healthcare, making the best possible predictions with complex models (e. g., neural networks, ensembles/stacks of different models) can impact patient welfare.

Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models

no code implementations ICLR 2019 Hugh Chen, Scott Lundberg, Gabe Erion, Su-In Lee

Here, we present the PHASE (PHysiologicAl Signal Embeddings) framework, which consists of three components: i) learning neural network embeddings of physiological signals, ii) predicting outcomes based on the learned embedding, and iii) interpreting the prediction results by estimating feature attributions in the "stacked" models (i. e., feature embedding model followed by prediction model).

Network Embedding

Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data

no code implementations23 Jan 2018 Hugh Chen, Scott Lundberg, Su-In Lee

In this paper, we present feature learning via long short term memory (LSTM) networks and prediction via gradient boosting trees (XGB).

Representation Learning Time Series

Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning

no code implementations2 Dec 2017 Gabriel Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee

We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values.

Checkpoint Ensembles: Ensemble Methods from a Single Training Process

no code implementations9 Oct 2017 Hugh Chen, Scott Lundberg, Su-In Lee

We present the checkpoint ensembles method that can learn ensemble models on a single training process.

Probabilistic Model-Based Approach for Heart Beat Detection

no code implementations24 Dec 2015 Hugh Chen, Yusuf Erol, Eric Shen, Stuart Russell

One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system.

Bayesian Inference

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