activity
20102026
most citedLearning under Concept Drift: an Overview

235 citations · 474 across the 10 of their papers we have counts for

collaborators

12 papers

cs.LG2026

What Do Learned Models Measure?

Indrė Žliobaitė

In many scientific and data-driven applications, machine learning models are increasingly used as measurement instruments, rather than merely as predictors of predefined labels. Wh…

q-bio.PE2024

Modelling the longevity of complex living systems

Indrė Žliobaitė

This extended abstract was presented at the Nectar Track of ECML PKDD 2024 in Vilnius, Lithuania. The content supplements a recently published paper "Laws of Macroevolutionary Expa…

cs.LG2023

A Historical Context for Data Streams

Indre Zliobaite, Jesse Read

Machine learning from data streams is an active and growing research area. Research on learning from streaming data typically makes strict assumptions linked to computational resou…

cs.AI2017★ 15 cited

Fairness-aware machine learning: a perspective

Indre Zliobaite

Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence th…

cs.LG2016

A note on adjusting for using with cross-validation

Indre Zliobaite, Nikolaj Tatti

We show how to adjust the coefficient of determination () when used for measuring predictive accuracy via leave-one-out cross-validation.

cs.CY2015★ 90 cited

A survey on measuring indirect discrimination in machine learning

Indre Zliobaite

Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process…