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Found 13 papers, 6 papers with code
Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts
Training machine learning models from data with weak supervision and dataset shifts is still challenging.
biquality-learn: a Python library for Biquality Learning
That is why Biquality Learning has been proposed as a machine learning framework to design algorithms capable of handling multiple weaknesses of supervision and dataset shifts without assumptions on their nature and level by relying on the availability of a small trusted dataset composed of cleanly labeled and representative samples.
Open challenges for Machine Learning based Early Decision-Making research
However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time.
When to Classify Events in Open Times Series?
In the Early Classification in Open Time Series problem (ECOTS), the task is to predict events, i. e. their class and time interval, at the moment that optimizes the accuracy vs. earliness trade-off.
Early and Revocable Time Series Classification
Many approaches have been proposed for early classification of time series in light of itssignificance in a wide range of applications including healthcare, transportation and fi-nance.
Contrastive Representations for Label Noise Require Fine-Tuning
In presence of noise the experiments show that fine tuning of Contrastive representation allows the six methods to achieve better results than end-to-end learning and represent a new reference compare to the recent state of art.
Early Classification of Time Series is Meaningful
Many approaches have been proposed for early classification of time series in light of its significance in a wide range of applications including healthcare, transportation and finance.
From Weakly Supervised Learning to Biquality Learning: an Introduction
The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies".
Predictive K-means with local models
We present two new algorithms using this technique and show on a variety of data sets that they are competitive for prediction performance with pure supervised classifiers while offering interpretability of the clusters discovered.
Importance Reweighting for Biquality Learning
The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies", namely: poor quality, non adaptability, and insufficient quantity of labels.
Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery and Education
Specifically, we put forward the hypothesis that two levels of knowledge, notional and conceptual, cooperate in the scientific discovery process when a new conceptual domain is being built.
