Search Results for author: Anna Goldenberg

Found 39 papers, 14 papers with code

Learning from Time Series under Temporal Label Noise

no code implementations6 Feb 2024 Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen

We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform.

Time Series

CongFu: Conditional Graph Fusion for Drug Synergy Prediction

1 code implementation23 May 2023 Oleksii Tsepa, Bohdan Naida, Anna Goldenberg, Bo wang

Drug synergy, characterized by the amplified combined effect of multiple drugs, is critically important for optimizing therapeutic outcomes.

Maintaining Stability and Plasticity for Predictive Churn Reduction

no code implementations6 May 2023 George Adam, Benjamin Haibe-Kains, Anna Goldenberg

Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time.

Dynamic Interpretable Change Point Detection

no code implementations8 Nov 2022 Kopal Garg, Jennifer Yu, Tina Behrouzi, Sana Tonekaboni, Anna Goldenberg

Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to potential risks or opportunities.

Change Point Detection Decision Making +2

Decoupling Local and Global Representations of Time Series

1 code implementation4 Feb 2022 Sana Tonekaboni, Chun-Liang Li, Sercan Arik, Anna Goldenberg, Tomas Pfister

Learning representations that capture the factors contributing to this variability enables a better understanding of the data via its underlying generative process and improves performance on downstream machine learning tasks.

counterfactual Time Series +1

Extracting Expert's Goals by What-if Interpretable Modeling

no code implementations28 Oct 2021 Chun-Hao Chang, George Alexandru Adam, Rich Caruana, Anna Goldenberg

Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed.

Additive models reinforcement-learning +1

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

2 code implementations ICLR 2022 Chun-Hao Chang, Rich Caruana, Anna Goldenberg

Deployment of machine learning models in real high-risk settings (e. g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability.

Additive models Fairness +1

Towards Robust Classification Model by Counterfactual and Invariant Data Generation

1 code implementation CVPR 2021 Chun-Hao Chang, George Alexandru Adam, Anna Goldenberg

Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions.

Classification counterfactual +3

Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds

no code implementations9 Nov 2020 Erik Drysdale, Devin Singh, Anna Goldenberg

Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting.

Time Series Time Series Forecasting

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings

no code implementations13 Oct 2020 Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi

Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data.

Fairness Mortality Prediction +3

How Interpretable and Trustworthy are GAMs?

2 code implementations11 Jun 2020 Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana

Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.

Additive models Inductive Bias +1

Using Generative Models for Pediatric wbMRI

no code implementations MIDL 2019 Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, Nipaporn Tewattanarat, Andrea Doria, Anna Goldenberg

Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics.

What went wrong and when? Instance-wise Feature Importance for Time-series Models

no code implementations5 Mar 2020 Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg

Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.

counterfactual Feature Importance +2

Explaining Time Series by Counterfactuals

no code implementations25 Sep 2019 Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg

We propose a method to automatically compute the importance of features at every observation in time series, by simulating counterfactual trajectories given previous observations.

counterfactual Feature Importance +2

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

1 code implementation2 Aug 2019 Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.

De-identification Length-of-Stay prediction +1

Reducing Adversarial Example Transferability Using Gradient Regularization

no code implementations16 Apr 2019 George Adam, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg

We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models.

The False Positive Control Lasso

2 code implementations29 Mar 2019 Erik Drysdale, Yingwei Peng, Timothy P. Hanna, Paul Nguyen, Anna Goldenberg

In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectation that most of the non-zero coefficients are associated with true signals.

Dynamic Measurement Scheduling for Event Forecasting using Deep RL

1 code implementation24 Jan 2019 Chun-Hao Chang, Mingjie Mai, Anna Goldenberg

We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements.

ICU Mortality Reinforcement Learning (RL) +1

Prediction of New Onset Diabetes after Liver Transplant

no code implementations3 Dec 2018 Angeline Yasodhara, Mamatha Bhat, Anna Goldenberg

Both patient's historical data and observations at the current visit are informative in predicting whether the patient will develop diabetes within the following year.

Time-to-Event Prediction

Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL

no code implementations1 Dec 2018 Chun-Hao Chang, Mingjie Mai, Anna Goldenberg

We address the scheduling problem using deep reinforcement learning (RL) to achieve high predictive gain and low measurement cost, by scheduling fewer, but strategically timed tests.

Reinforcement Learning (RL) Scheduling

Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

no code implementations30 Nov 2018 Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time.

BIG-bench Machine Learning Mortality Prediction

Explaining Image Classifiers by Counterfactual Generation

1 code implementation ICLR 2019 Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?

counterfactual Image Classification

Dropout Feature Ranking for Deep Learning Models

1 code implementation22 Dec 2017 Chun-Hao Chang, Ladislav Rampasek, Anna Goldenberg

Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains.

Time Series Time Series Analysis

Dr.VAE: Drug Response Variational Autoencoder

no code implementations26 Jun 2017 Ladislav Rampasek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg

We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction.

Drug Response Prediction General Classification

EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization

no code implementations14 Sep 2014 Daniel Hidru, Anna Goldenberg

Nonnegative matrix factorization (NMF) methods have proved to be powerful across a wide range of real-world clustering applications.

Clustering Data Integration

Gradient-based Laplacian Feature Selection

no code implementations10 Apr 2014 Bo Wang, Anna Goldenberg

With $\ell_1$ relaxation, GLFS can find a sparse subset of features that is relevant to the Laplacian manifolds.

feature selection Object Recognition

A survey of statistical network models

no code implementations29 Dec 2009 Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi

Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation.


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