73 citations · 124 across the 9 of their papers we have counts for
9 papers
A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Dario Rancati +3
As learning systems increasingly shape everyday decisions, Algorithmic Collective Action (ACA), i.e., users coordinating changes to shared data to steer model behavior, offers a co…
Positive-Unlabelled Active Learning to Curate a Dataset for Orca Resident Interpretation
Bret Nestor, Bohan Yao, Jasmine Moore +1
This work presents the largest curation of Southern Resident Killer Whale (SRKW) acoustic data to date, also containing other marine mammals in their environment. We systematically…
Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Bret Nestor +2
As learning systems increasingly influence everyday decisions, user-side steering via Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to…
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
Matthew B. A. McDermott, Bret Nestor, Peniel Argaw +1
Generative, pre-trained transformers (GPTs, a.k.a. "Foundation Models") have reshaped natural language processing (NLP) through their versatility in diverse downstream tasks. Howev…
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
Matthew B. A. McDermott, Bret Nestor, Evan Kim +4
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks. It has been used extensively on various d…
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott +3
Multi-language speech datasets are scarce and often have small sample sizes in the medical domain. Robust transfer of linguistic features across languages could improve rates of ea…
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
Bret Nestor, Matthew B. A. McDermott, Willie Boag +5
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 ca…
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan +4
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 operat…