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Found 9 papers, 2 papers with code
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion.
Learning from Time Series under Temporal Label Noise
We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform.
Dynamic Interpretable Change Point Detection
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.
Decoupling Local and Global Representations of Time Series
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.
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model.
What went wrong and when?\\ Instance-wise feature importance for time-series black-box models
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.
What went wrong and when? Instance-wise Feature Importance for Time-series Models
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.
Explaining Time Series by Counterfactuals
We propose a method to automatically compute the importance of features at every observation in time series, by simulating counterfactual trajectories given previous observations.
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust.
