Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Horváth, Miklós Z."'
Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored, i
Externí odkaz:
http://arxiv.org/abs/2205.13909
Randomized Smoothing (RS) is considered the state-of-the-art approach to obtain certifiably robust models for challenging tasks. However, current RS approaches drastically decrease standard accuracy on unperturbed data, severely limiting their real-w
Externí odkaz:
http://arxiv.org/abs/2204.00487
Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work, we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii
Externí odkaz:
http://arxiv.org/abs/2106.06946
Publikováno v:
The Tenth International Conference on Learning Representations (ICLR 2022)
Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work, we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e84abdaa42b367cced8a9087e23a5b7
https://hdl.handle.net/20.500.11850/592011
https://hdl.handle.net/20.500.11850/592011