Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Okati, Nastaran"'
Autor:
Okati, Nastaran, Mejia, Sergio Hernan Garrido, Orchard, William Roy, Blöbaum, Patrick, Janzing, Dominik
Recent work conceptualized root cause analysis (RCA) of anomalies via quantitative contribution analysis using causal counterfactuals in structural causal models (SCMs). The framework comes with three practical challenges: (1) it requires the causal
Externí odkaz:
http://arxiv.org/abs/2406.05014
Autor:
De Toni, Giovanni, Okati, Nastaran, Thejaswi, Suhas, Straitouri, Eleni, Gomez-Rodriguez, Manuel
Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. Rather than providing single-label predictions, these systems provide sets of label predictions constructed using confo
Externí odkaz:
http://arxiv.org/abs/2405.17544
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier is calibrated, one can identify the smallest set of candidates which c
Externí odkaz:
http://arxiv.org/abs/2302.00025
Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exe
Externí odkaz:
http://arxiv.org/abs/2201.12006
Multiple lines of evidence suggest that predictive models may benefit from algorithmic triage. Under algorithmic triage, a predictive model does not predict all instances but instead defers some of them to human experts. However, the interplay betwee
Externí odkaz:
http://arxiv.org/abs/2103.08902
Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by this empirical observation, our goal is to design classifiers that ar
Externí odkaz:
http://arxiv.org/abs/2006.11845
Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation -- they are not aware that some of the decisions may still be taken by humans. In this paper, we t
Externí odkaz:
http://arxiv.org/abs/1909.02963
We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables. First, we show that several examples fr
Externí odkaz:
http://arxiv.org/abs/1804.08984
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier is calibrated, one can identify the smallest set of candidates which c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::100a6e37bc172b6fd13347bdf19fedc4
http://arxiv.org/abs/2302.00025
http://arxiv.org/abs/2302.00025