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Found 16 papers, 2 papers with code
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
A hierarchical decomposition for explaining ML performance discrepancies
Machine learning (ML) algorithms can often differ in performance across domains.
A Brief Tutorial on Sample Size Calculations for Fairness Audits
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups.
Machine Learning for Health symposium 2023 -- Findings track
A collection of the accepted Findings papers that were presented at the 3rd Machine Learning for Health symposium (ML4H 2023), which was held on December 10, 2023, in New Orleans, Louisiana, USA.
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
In this work, we introduce the problem of attributing performance differences between environments to distribution shifts in the underlying data generating mechanisms.
Towards Robust Off-Policy Evaluation via Human Inputs
When deployment environments are expected to undergo changes (that is, dataset shifts), it is important for OPE methods to perform robust evaluation of the policies amidst such changes.
Segmenting across places: The need for fair transfer learning with satellite imagery
This work highlights the need to conduct fairness analysis for satellite imagery segmentation models and motivates the development of methods for fair transfer learning in order not to introduce disparities between places, particularly urban and rural locations.
Learning Under Adversarial and Interventional Shifts
Most of the existing work focuses on optimizing for either adversarial shifts or interventional shifts.
An RNN-Survival Model to Decide Email Send Times
We posit that emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored.
Fairness Violations and Mitigation under Covariate Shift
We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set.
Modeling Time to Open of Emails with a Latent State for User Engagement Level
In this paper we use a survival analysis framework to predict the time to open an email once it has been received.
Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts
Data analytics software applications have become an integral part of the decision-making process of analysts.
Online Diverse Learning to Rank from Partial-Click Feedback
We assume that the user examines the list of recommended items until the user is attracted by an item, which is clicked, and does not examine the rest of the items.
Show and Recall: Learning What Makes Videos Memorable
With the explosion of video content on the Internet, there is a need for research on methods for video analysis which take human cognition into account.
