Search Results for author: Katherine Heller

Found 29 papers, 6 papers with code

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa

no code implementations5 Mar 2024 Mercy Asiedu, Awa Dieng, Iskandar Haykel, Negar Rostamzadeh, Stephen Pfohl, Chirag Nagpal, Maria Nagawa, Abigail Oppong, Sanmi Koyejo, Katherine Heller

Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism.

Attribute Fairness

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

no code implementations14 Dec 2023 Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D'Amour, DJ Dvijotham, Adam Fisch, Katherine Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, Jonathan Berant

However, even pretrain reward ensembles do not eliminate reward hacking: we show several qualitative reward hacking phenomena that are not mitigated by ensembling because all reward models in the ensemble exhibit similar error patterns.

Language Modelling

STUDY: Socially Aware Temporally Causal Decoder Recommender Systems

no code implementations2 Jun 2023 Eltayeb Ahmed, Diana Mincu, Lauren Harrell, Katherine Heller, Subhrajit Roy

We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers.

Recommendation Systems

Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa

no code implementations5 Apr 2023 Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong, Maria Nagawa, Sanmi Koyejo, Katherine Heller

With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose.


Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression

no code implementations15 Feb 2023 Alexander Norcliffe, Lev Proleev, Diana Mincu, Fletcher Lee Hartsell, Katherine Heller, Subhrajit Roy

Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure.


Evaluation Gaps in Machine Learning Practice

no code implementations11 May 2022 Ben Hutchinson, Negar Rostamzadeh, Christina Greer, Katherine Heller, Vinodkumar Prabhakaran

Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and responsibilities.

BIG-bench Machine Learning

Disability prediction in multiple sclerosis using performance outcome measures and demographic data

no code implementations8 Apr 2022 Subhrajit Roy, Diana Mincu, Lev Proleev, Negar Rostamzadeh, Chintan Ghate, Natalie Harris, Christina Chen, Jessica Schrouff, Nenad Tomasev, Fletcher Lee Hartsell, Katherine Heller

To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-base studies by using two datasets.

Benchmarking BIG-bench Machine Learning

Healthsheet: Development of a Transparency Artifact for Health Datasets

1 code implementation26 Feb 2022 Negar Rostamzadeh, Diana Mincu, Subhrajit Roy, Andrew Smart, Lauren Wilcox, Mahima Pushkarna, Jessica Schrouff, Razvan Amironesei, Nyalleng Moorosi, Katherine Heller

Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.

Deep Cox Mixtures for Survival Regression

4 code implementations16 Jan 2021 Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh, Katherine Heller

Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up.

regression Survival Analysis

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

1 code implementation ICML 2020 Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning.

Uncertainty Quantification

Federated and Differentially Private Learning for Electronic Health Records

no code implementations13 Nov 2019 Stephen R. Pfohl, Andrew M. Dai, Katherine Heller

The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository.

Federated Learning

Analyzing the Role of Model Uncertainty for Electronic Health Records

1 code implementation10 Jun 2019 Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai

We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.

Modulating transfer between tasks in gradient-based meta-learning

no code implementations ICLR 2019 Erin Grant, Ghassen Jerfel, Katherine Heller, Thomas L. Griffiths

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task.

Inductive Bias Meta-Learning

Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients

1 code implementation24 Jul 2018 Elizabeth Lorenzi, Ricardo Henao, Katherine Heller

We develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data.


Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks

no code implementations ICLR 2018 Joseph Futoma, Anthony Lin, Mark Sendak, Armando Bedoya, Meredith Clement, Cara O'Brien, Katherine Heller

We evaluate our approach on a heterogeneous dataset of septic spanning 15 months from our university health system, and find that our learned policy could reduce patient mortality by as much as 8. 2\% from an overall baseline mortality rate of 13. 3\%.

Gaussian Processes reinforcement-learning +3

An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection

no code implementations19 Aug 2017 Joseph Futoma, Sanjay Hariharan, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien, Katherine Heller

Latent function values from the Gaussian process are then fed into a deep recurrent neural network to classify patient encounters as septic or not, and the overall model is trained end-to-end using back-propagation.

Gaussian Processes Time Series Analysis

Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

2 code implementations ICML 2017 Joseph Futoma, Sanjay Hariharan, Katherine Heller

We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity.

Gaussian Processes Time Series +1

Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression

no code implementations16 Aug 2016 Joseph Futoma, Mark Sendak, C. Blake Cameron, Katherine Heller

Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management.

Management Variational Inference

$k$-means: Fighting against Degeneracy in Sequential Monte Carlo with an Application to Tracking

no code implementations13 Nov 2015 Kai Fan, Katherine Heller

Specifically, we propose a Stochastic SMC algorithm which initializes the set of $k$ means, providing the initial centers chosen from the collapsed particles.


Fast Second-Order Stochastic Backpropagation for Variational Inference

no code implementations9 Sep 2015 Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine Heller

We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed as well.

regression Variational Inference

Ranking relations using analogies in biological and information networks

no code implementations28 Dec 2009 Ricardo Silva, Katherine Heller, Zoubin Ghahramani, Edoardo M. Airoldi

Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?

Information Retrieval Relational Reasoning +1

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