15 citations · 53 across the 16 of their papers we have counts for
7 papers · 1 filter
Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood
Weisong Yang, Rafael Poyiadzi, Niall Twomey +1
In supervised learning, automatically assessing the quality of the labels before any learning takes place remains an open research question. In certain particular cases, hypothesis…
Low-count Time Series Anomaly Detection
Philipp Renz, Kurt Cutajar, Niall Twomey +2
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct chal…
Hypothesis Testing for Class-Conditional Label Noise
Rafael Poyiadzi, Weisong Yang, Niall Twomey +1
In this paper we provide machine learning practitioners with tools to answer the question: is there class-conditional noise in my labels? In particular, we present hypothesis tests…
HyperStream: a Workflow Engine for Streaming Data
Tom Diethe, Meelis Kull, Niall Twomey +5
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabili…
Ordinal Regression as Structured Classification
Niall Twomey, Rafael Poyiadzi, Callum Mann +1
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels. The net effect of this is…
Neural ODEs with stochastic vector field mixtures
Niall Twomey, Michał Kozłowski, Raúl Santos-Rodríguez
It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representa…
Label Propagation for Learning with Label Proportions
Rafael Poyiadzi, Raul Santos-Rodriguez, Niall Twomey
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is parti…