Search Results for author:
Found 36 papers, 9 papers with code
Opportunities for Human-centered Evaluation of Machine Translation Systems
Machine translation models are embedded in larger user-facing systems.
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners.
EraseBench: Understanding The Ripple Effects of Concept Erasure Techniques
This highlights the gap in reliability of the concept erasure techniques.
Speech Recognition With LLMs Adapted to Disordered Speech Using Reinforcement Learning
We introduce a large language model (LLM) capable of processing speech inputs and show that tuning it further with reinforcement learning on human preference (RLHF) enables it to adapt better to disordered speech than traditional fine-tuning.
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally.
Contextual Evaluation of Large Language Models for Classifying Tropical and Infectious Diseases
While large language models (LLMs) have shown promise for medical question answering, there is limited work focused on tropical and infectious disease-specific exploration.
Nteasee: Understanding Needs in AI for Health in Africa -- A Mixed-Methods Study of Expert and General Population Perspectives
This work presents the first-of-its-kind qualitative research study of the potential of AI for health in Africa from an algorithmic fairness angle, with perspectives from both experts and the general population.
Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
RESULTS: In the evaluation on the held-out test set, the model demonstrated achieved an AUC of 0. 65.
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases, and EquityMedQA, a collection of seven datasets enriched for adversarial queries.
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
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.
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
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.
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs).
STUDY: Socially Aware Temporally Causal Decoder Recommender Systems
We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers.
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
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
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
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.
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
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.
Healthsheet: Development of a Transparency Artifact for Health Datasets
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.
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
Deep Cox Mixtures for Survival Regression
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.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning.
Federated and Differentially Private Learning for Electronic Health Records
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.
Analyzing the Role of Model Uncertainty for Electronic Health Records
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
Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task.
Reconciling meta-learning and continual learning with online mixtures of tasks
Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task.
Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients
We develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data.
Applications
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks
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\%.
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the inverse problems.
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
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.
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
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.
Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management.
$k$-means: Fighting against Degeneracy in Sequential Monte Carlo with an Application to Tracking
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
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.
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data.
Ranking relations using analogies in biological and information networks
Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?
