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Found 7 papers, 0 papers with code
Categorizing Items with Short and Noisy Descriptions using Ensembled Transferred Embeddings
In this work, we focus on item categorization settings in which the textual attributes representing items are noisy and short, and labels (i. e., accurate classification of items into categories) are not available.
Fairness-Driven Private Collaborative Machine Learning
The performance of machine learning algorithms can be considerably improved when trained over larger datasets.
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles
Finally, the set of subspaces is used in an ensemble for anomaly detection.
Improving Label Ranking Ensembles using Boosting Techniques
Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i. e., order) of labels from a finite set, representing their relevance to the instance.
Algorithmic Fairness
The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness.
Handwritten Signature Verification Using Hand-Worn Devices
Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures.
Human collective intelligence as distributed Bayesian inference
To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference.
